WorldWideScience

Sample records for supervised image classification

  1. Supervised Classification Performance of Multispectral Images

    OpenAIRE

    Perumal, K.; Bhaskaran, R.

    2010-01-01

    Nowadays government and private agencies use remote sensing imagery for a wide range of applications from military applications to farm development. The images may be a panchromatic, multispectral, hyperspectral or even ultraspectral of terra bytes. Remote sensing image classification is one amongst the most significant application worlds for remote sensing. A few number of image classification algorithms have proved good precision in classifying remote sensing data. But, of late, due to the ...

  2. Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

    Science.gov (United States)

    Jamshidpour, N.; Homayouni, S.; Safari, A.

    2017-09-01

    Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

  3. GRAPH-BASED SEMI-SUPERVISED HYPERSPECTRAL IMAGE CLASSIFICATION USING SPATIAL INFORMATION

    Directory of Open Access Journals (Sweden)

    N. Jamshidpour

    2017-09-01

    Full Text Available Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

  4. A review of supervised object-based land-cover image classification

    Science.gov (United States)

    Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

    2017-08-01

    Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial

  5. Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Zhi He

    2017-10-01

    Full Text Available Classification of hyperspectral image (HSI is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs. Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples.

  6. Multi-Modal Curriculum Learning for Semi-Supervised Image Classification.

    Science.gov (United States)

    Gong, Chen; Tao, Dacheng; Maybank, Stephen J; Liu, Wei; Kang, Guoliang; Yang, Jie

    2016-07-01

    Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.

  7. Non supervised classification of vegetable covers on digital images of remote sensors: Landsat - ETM+

    International Nuclear Information System (INIS)

    Arango Gutierrez, Mauricio; Branch Bedoya, John William; Botero Fernandez, Veronica

    2005-01-01

    The plant species diversity in Colombia and the lack of inventory of them suggests the need for a process that facilitates the work of investigators in these disciplines. Remote satellite sensors such as landsat ETM+ and non-supervised artificial intelligence techniques, such as self-organizing maps - SOM, could provide viable alternatives for advancing in the rapid obtaining of information related to zones with different vegetative covers in the national geography. The zone proposed for the study case was classified in a supervised form by the method of maximum likelihood by another investigation in forest sciences and eight types of vegetative covers were discriminated. This information served as a base line to evaluate the performance of the non-supervised sort keys isodata and SOM. However, the information that the images provided had to first be purified according to the criteria of use and data quality, so that adequate information for these non-supervised methods were used. For this, several concepts were used; such as, image statistics, spectral behavior of the vegetative communities, sensor characteristics and the average divergence that allowed to define the best bands and their combinations. Principal component analysis was applied to these to reduce to the number of data while conserving a large percentage of the information. The non-supervised techniques were applied to these purified data, modifying some parameters that could yield a better convergence of the methods. The results obtained were compared with the supervised classification via confusion matrices and it was concluded that there was not a good convergence of non-supervised classification methods with this process for the case of vegetative covers

  8. Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment

    Directory of Open Access Journals (Sweden)

    Radoi Emanuel

    2006-01-01

    Full Text Available The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART, which is compared to two standard classifiers, MLP (multilayer perceptron and fuzzy KNN ( nearest neighbors. While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure.

  9. Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification.

    Science.gov (United States)

    Hu, Zhihong; Medioni, Gerard G; Hernandez, Matthias; Sadda, Srinivas R

    2015-01-01

    Geographic atrophy (GA) is a manifestation of the advanced or late stage of age-related macular degeneration (AMD). AMD is the leading cause of blindness in people over the age of 65 in the western world. The purpose of this study is to develop a fully automated supervised pixel classification approach for segmenting GA, including uni- and multifocal patches in fundus autofluorescene (FAF) images. The image features include region-wise intensity measures, gray-level co-occurrence matrix measures, and Gaussian filter banks. A [Formula: see text]-nearest-neighbor pixel classifier is applied to obtain a GA probability map, representing the likelihood that the image pixel belongs to GA. Sixteen randomly chosen FAF images were obtained from 16 subjects with GA. The algorithm-defined GA regions are compared with manual delineation performed by a certified image reading center grader. Eight-fold cross-validation is applied to evaluate the algorithm performance. The mean overlap ratio (OR), area correlation (Pearson's [Formula: see text]), accuracy (ACC), true positive rate (TPR), specificity (SPC), positive predictive value (PPV), and false discovery rate (FDR) between the algorithm- and manually defined GA regions are [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively.

  10. A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification.

    Science.gov (United States)

    Peikari, Mohammad; Salama, Sherine; Nofech-Mozes, Sharon; Martel, Anne L

    2018-05-08

    Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the possibility of using clustering analysis to identify the underlying structure of the data space for SSL. A cluster-then-label method was proposed to identify high-density regions in the data space which were then used to help a supervised SVM in finding the decision boundary. We have compared our method with other supervised and semi-supervised state-of-the-art techniques using two different classification tasks applied to breast pathology datasets. We found that compared with other state-of-the-art supervised and semi-supervised methods, our SSL method is able to improve classification performance when a limited number of labeled data instances are made available. We also showed that it is important to examine the underlying distribution of the data space before applying SSL techniques to ensure semi-supervised learning assumptions are not violated by the data.

  11. Automatic segmentation of MR brain images of preterm infants using supervised classification.

    Science.gov (United States)

    Moeskops, Pim; Benders, Manon J N L; Chiţ, Sabina M; Kersbergen, Karina J; Groenendaal, Floris; de Vries, Linda S; Viergever, Max A; Išgum, Ivana

    2015-09-01

    Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data

  12. An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

    Science.gov (United States)

    Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

    2014-01-01

    The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

  13. A new tool for supervised classification of satellite images available on web servers: Google Maps as a case study

    Science.gov (United States)

    García-Flores, Agustín.; Paz-Gallardo, Abel; Plaza, Antonio; Li, Jun

    2016-10-01

    This paper describes a new web platform dedicated to the classification of satellite images called Hypergim. The current implementation of this platform enables users to perform classification of satellite images from any part of the world thanks to the worldwide maps provided by Google Maps. To perform this classification, Hypergim uses unsupervised algorithms like Isodata and K-means. Here, we present an extension of the original platform in which we adapt Hypergim in order to use supervised algorithms to improve the classification results. This involves a significant modification of the user interface, providing the user with a way to obtain samples of classes present in the images to use in the training phase of the classification process. Another main goal of this development is to improve the runtime of the image classification process. To achieve this goal, we use a parallel implementation of the Random Forest classification algorithm. This implementation is a modification of the well-known CURFIL software package. The use of this type of algorithms to perform image classification is widespread today thanks to its precision and ease of training. The actual implementation of Random Forest was developed using CUDA platform, which enables us to exploit the potential of several models of NVIDIA graphics processing units using them to execute general purpose computing tasks as image classification algorithms. As well as CUDA, we use other parallel libraries as Intel Boost, taking advantage of the multithreading capabilities of modern CPUs. To ensure the best possible results, the platform is deployed in a cluster of commodity graphics processing units (GPUs), so that multiple users can use the tool in a concurrent way. The experimental results indicate that this new algorithm widely outperform the previous unsupervised algorithms implemented in Hypergim, both in runtime as well as precision of the actual classification of the images.

  14. Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function

    Directory of Open Access Journals (Sweden)

    Chunyan Wang

    2018-05-01

    Full Text Available Because of the degradation of classification accuracy that is caused by the uncertainty of pixel class and classification decisions of high-resolution remote-sensing images, we proposed a supervised classification method that is based on an interval type-2 fuzzy membership function for high-resolution remote-sensing images. We analyze the data features of a high-resolution remote-sensing image and construct a type-1 membership function model in a homogenous region by supervised sampling in order to characterize the uncertainty of the pixel class. On the basis of the fuzzy membership function model in the homogeneous region and in accordance with the 3σ criterion of normal distribution, we proposed a method for modeling three types of interval type-2 membership functions and analyze the different types of functions to improve the uncertainty of pixel class expressed by the type-1 fuzzy membership function and to enhance the accuracy of classification decision. According to the principle that importance will increase with a decrease in the distance between the original, upper, and lower fuzzy membership of the training data and the corresponding frequency value in the histogram, we use the weighted average sum of three types of fuzzy membership as the new fuzzy membership of the pixel to be classified and then integrated into the neighborhood pixel relations, constructing a classification decision model. We use the proposed method to classify real high-resolution remote-sensing images and synthetic images. Additionally, we qualitatively and quantitatively evaluate the test results. The results show that a higher classification accuracy can be achieved with the proposed algorithm.

  15. Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

    Science.gov (United States)

    Zhou, Fuqun; Zhang, Aining

    2016-10-25

    Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

  16. A Novel Approach to Developing a Supervised Spatial Decision Support System for Image Classification: A Study of Paddy Rice Investigation

    Directory of Open Access Journals (Sweden)

    Shih-Hsun Chang

    2014-01-01

    Full Text Available Paddy rice area estimation via remote sensing techniques has been well established in recent years. Texture information and vegetation indicators are widely used to improve the classification accuracy of satellite images. Accordingly, this study employs texture information and vegetation indicators as ancillary information for classifying paddy rice through remote sensing images. In the first stage, the images are attained using a remote sensing technique and ancillary information is employed to increase the accuracy of classification. In the second stage, we decide to construct an efficient supervised classifier, which is used to evaluate the ancillary information. In the third stage, linear discriminant analysis (LDA is introduced. LDA is a well-known method for classifying images to various categories. Also, the particle swarm optimization (PSO algorithm is employed to optimize the LDA classification outcomes and increase classification performance. In the fourth stage, we discuss the strategy of selecting different window sizes and analyze particle numbers and iteration numbers with corresponding accuracy. Accordingly, a rational strategy for the combination of ancillary information is introduced. Afterwards, the PSO algorithm improves the accuracy rate from 82.26% to 89.31%. The improved accuracy results in a much lower salt-and-pepper effect in the thematic map.

  17. Manifold regularized multitask learning for semi-supervised multilabel image classification.

    Science.gov (United States)

    Luo, Yong; Tao, Dacheng; Geng, Bo; Xu, Chao; Maybank, Stephen J

    2013-02-01

    It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.

  18. Improving supervised classification accuracy using non-rigid multimodal image registration: detecting prostate cancer

    Science.gov (United States)

    Chappelow, Jonathan; Viswanath, Satish; Monaco, James; Rosen, Mark; Tomaszewski, John; Feldman, Michael; Madabhushi, Anant

    2008-03-01

    Computer-aided diagnosis (CAD) systems for the detection of cancer in medical images require precise labeling of training data. For magnetic resonance (MR) imaging (MRI) of the prostate, training labels define the spatial extent of prostate cancer (CaP); the most common source for these labels is expert segmentations. When ancillary data such as whole mount histology (WMH) sections, which provide the gold standard for cancer ground truth, are available, the manual labeling of CaP can be improved by referencing WMH. However, manual segmentation is error prone, time consuming and not reproducible. Therefore, we present the use of multimodal image registration to automatically and accurately transcribe CaP from histology onto MRI following alignment of the two modalities, in order to improve the quality of training data and hence classifier performance. We quantitatively demonstrate the superiority of this registration-based methodology by comparing its results to the manual CaP annotation of expert radiologists. Five supervised CAD classifiers were trained using the labels for CaP extent on MRI obtained by the expert and 4 different registration techniques. Two of the registration methods were affi;ne schemes; one based on maximization of mutual information (MI) and the other method that we previously developed, Combined Feature Ensemble Mutual Information (COFEMI), which incorporates high-order statistical features for robust multimodal registration. Two non-rigid schemes were obtained by succeeding the two affine registration methods with an elastic deformation step using thin-plate splines (TPS). In the absence of definitive ground truth for CaP extent on MRI, classifier accuracy was evaluated against 7 ground truth surrogates obtained by different combinations of the expert and registration segmentations. For 26 multimodal MRI-WMH image pairs, all four registration methods produced a higher area under the receiver operating characteristic curve compared to that

  19. Improvements on coronal hole detection in SDO/AIA images using supervised classification

    Directory of Open Access Journals (Sweden)

    Reiss Martin A.

    2015-01-01

    Full Text Available We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA, we prepared datasets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011–2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine (SVM, Linear Support Vector Machine, Decision Tree, and Random Forest, and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ≈ 0.90. Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.

  20. Automatic segmentation of MR brain images of preterm infants using supervised classification

    NARCIS (Netherlands)

    Moeskops, Pim; Benders, Manon J N L; Chiţă, Sabina M.; Kersbergen, Karina J.; Groenendaal, Floris; de Vries, Linda S.; Viergever, Max A.; Isgum, Ivana

    Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images.This paper

  1. Automatic segmentation of MR brain images of preterm infants using supervised classification

    NARCIS (Netherlands)

    Moeskops, P.; Benders, M.J.N.L.; Chiţ, S.M.; Kersbergen, K.J.; Groenendaal, F.; de Vries, L.S.; Viergever, M.A.; Išgum, I.

    Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper

  2. Weakly supervised classification in high energy physics

    International Nuclear Information System (INIS)

    Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco; Schwartzman, Ariel

    2017-01-01

    As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics — quark versus gluon tagging — we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

  3. Weakly supervised classification in high energy physics

    Energy Technology Data Exchange (ETDEWEB)

    Dery, Lucio Mwinmaarong [Physics Department, Stanford University,Stanford, CA, 94305 (United States); Nachman, Benjamin [Physics Division, Lawrence Berkeley National Laboratory,1 Cyclotron Rd, Berkeley, CA, 94720 (United States); Rubbo, Francesco; Schwartzman, Ariel [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA, 94025 (United States)

    2017-05-29

    As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics — quark versus gluon tagging — we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

  4. Classification of remotely sensed images

    CSIR Research Space (South Africa)

    Dudeni, N

    2008-10-01

    Full Text Available For this research, the researchers examine various existing image classification algorithms with the aim of demonstrating how these algorithms can be applied to remote sensing images. These algorithms are broadly divided into supervised...

  5. Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques.

    Science.gov (United States)

    Feng, Jingwen; Feng, Tong; Yang, Chengwen; Wang, Wei; Sa, Yu; Feng, Yuanming

    2018-06-01

    This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining. The texture features of the diffraction images were extracted with in-house software based on the Gray-level co-occurrence matrix algorithm to generate datasets for cell classification with supervised machine learning method. Therefore, this new method has been verified in hydrogen peroxide induced apoptosis model of HL-60. Results show that accuracy of higher than 90% was achieved respectively in independent test datasets from each cell type based on logistic regression with ridge estimators, which indicated that p-DIFC system has a great potential in predicting and classifying cells in different stages of apoptosis.

  6. Classification of JERS-1 Image Mosaic of Central Africa Using A Supervised Multiscale Classifier of Texture Features

    Science.gov (United States)

    Saatchi, Sassan; DeGrandi, Franco; Simard, Marc; Podest, Erika

    1999-01-01

    In this paper, a multiscale approach is introduced to classify the Japanese Research Satellite-1 (JERS-1) mosaic image over the Central African rainforest. A series of texture maps are generated from the 100 m mosaic image at various scales. Using a quadtree model and relating classes at each scale by a Markovian relationship, the multiscale images are classified from course to finer scale. The results are verified at various scales and the evolution of classification is monitored by calculating the error at each stage.

  7. Observation versus classification in supervised category learning.

    Science.gov (United States)

    Levering, Kimery R; Kurtz, Kenneth J

    2015-02-01

    The traditional supervised classification paradigm encourages learners to acquire only the knowledge needed to predict category membership (a discriminative approach). An alternative that aligns with important aspects of real-world concept formation is learning with a broader focus to acquire knowledge of the internal structure of each category (a generative approach). Our work addresses the impact of a particular component of the traditional classification task: the guess-and-correct cycle. We compare classification learning to a supervised observational learning task in which learners are shown labeled examples but make no classification response. The goals of this work sit at two levels: (1) testing for differences in the nature of the category representations that arise from two basic learning modes; and (2) evaluating the generative/discriminative continuum as a theoretical tool for understand learning modes and their outcomes. Specifically, we view the guess-and-correct cycle as consistent with a more discriminative approach and therefore expected it to lead to narrower category knowledge. Across two experiments, the observational mode led to greater sensitivity to distributional properties of features and correlations between features. We conclude that a relatively subtle procedural difference in supervised category learning substantially impacts what learners come to know about the categories. The results demonstrate the value of the generative/discriminative continuum as a tool for advancing the psychology of category learning and also provide a valuable constraint for formal models and associated theories.

  8. Phenotype classification of zebrafish embryos by supervised learning.

    Directory of Open Access Journals (Sweden)

    Nathalie Jeanray

    Full Text Available Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  9. Supervised Learning for Visual Pattern Classification

    Science.gov (United States)

    Zheng, Nanning; Xue, Jianru

    This chapter presents an overview of the topics and major ideas of supervised learning for visual pattern classification. Two prevalent algorithms, i.e., the support vector machine (SVM) and the boosting algorithm, are briefly introduced. SVMs and boosting algorithms are two hot topics of recent research in supervised learning. SVMs improve the generalization of the learning machine by implementing the rule of structural risk minimization (SRM). It exhibits good generalization even when little training data are available for machine training. The boosting algorithm can boost a weak classifier to a strong classifier by means of the so-called classifier combination. This algorithm provides a general way for producing a classifier with high generalization capability from a great number of weak classifiers.

  10. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    Science.gov (United States)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  11. QUEST : Eliminating online supervised learning for efficient classification algorithms

    NARCIS (Netherlands)

    Zwartjes, Ardjan; Havinga, Paul J.M.; Smit, Gerard J.M.; Hurink, Johann L.

    2016-01-01

    In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting

  12. Transfer learning improves supervised image segmentation across imaging protocols.

    Science.gov (United States)

    van Opbroek, Annegreet; Ikram, M Arfan; Vernooij, Meike W; de Bruijne, Marleen

    2015-05-01

    The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

  13. Projected estimators for robust semi-supervised classification

    NARCIS (Netherlands)

    Krijthe, J.H.; Loog, M.

    2017-01-01

    For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the

  14. Genetic classification of populations using supervised learning.

    Directory of Open Access Journals (Sweden)

    Michael Bridges

    2011-05-01

    Full Text Available There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories. This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines to the classification of three populations (two from Scotland and one from Bulgaria. The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.

  15. Genetic classification of populations using supervised learning.

    LENUS (Irish Health Repository)

    Bridges, Michael

    2011-01-01

    There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.

  16. Automatic Hierarchical Color Image Classification

    Directory of Open Access Journals (Sweden)

    Jing Huang

    2003-02-01

    Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.

  17. Optimistic semi-supervised least squares classification

    DEFF Research Database (Denmark)

    Krijthe, Jesse H.; Loog, Marco

    2017-01-01

    The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant ...

  18. Transfer learning improves supervised image segmentation across imaging protocols

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.

    2015-01-01

    with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two MRI brain-segmentation tasks with multi-site data: white matter, gray matter, and CSF segmentation; and white-matter- /MS-lesion segmentation......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore...

  19. Projected estimators for robust semi-supervised classification

    DEFF Research Database (Denmark)

    Krijthe, Jesse H.; Loog, Marco

    2017-01-01

    For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure...... specifically, we prove that, measured on the labeled and unlabeled training data, this semi-supervised procedure never gives a lower quadratic loss than the supervised alternative. To our knowledge this is the first approach that offers such strong, albeit conservative, guarantees for improvement over...... the supervised solution. The characteristics of our approach are explicated using benchmark datasets to further understand the similarities and differences between the quadratic loss criterion used in the theoretical results and the classification accuracy typically considered in practice....

  20. Supervised Gaussian mixture model based remote sensing image ...

    African Journals Online (AJOL)

    Using the supervised classification technique, both simulated and empirical satellite remote sensing data are used to train and test the Gaussian mixture model algorithm. For the purpose of validating the experiment, the resulting classified satellite image is compared with the ground truth data. For the simulated modelling, ...

  1. Supervised classification of continental shelf sediment off western Donegal, Ireland

    Science.gov (United States)

    Monteys, X.; Craven, K.; McCarron, S. G.

    2017-12-01

    Managing human impacts on marine ecosystems requires natural regions to be identified and mapped over a range of hierarchically nested scales. In recent years (2000-present) the Irish National Seabed Survey (INSS) and Integrated Mapping for the Sustainable Development of Ireland's Marine Resources programme (INFOMAR) (Geological Survey Ireland and Marine Institute collaborations) has provided unprecedented quantities of high quality data on Ireland's offshore territories. The increasing availability of large, detailed digital representations of these environments requires the application of objective and quantitative analyses. This study presents results of a new approach for sea floor sediment mapping based on an integrated analysis of INFOMAR multibeam bathymetric data (including the derivatives of slope and relative position), backscatter data (including derivatives of angular response analysis) and sediment groundtruthing over the continental shelf, west of Donegal. It applies a Geographic-Object-Based Image Analysis software package to provide a supervised classification of the surface sediment. This approach can provide a statistically robust, high resolution classification of the seafloor. Initial results display a differentiation of sediment classes and a reduction in artefacts from previously applied methodologies. These results indicate a methodology that could be used during physical habitat mapping and classification of marine environments.

  2. Weakly supervised visual dictionary learning by harnessing image attributes.

    Science.gov (United States)

    Gao, Yue; Ji, Rongrong; Liu, Wei; Dai, Qionghai; Hua, Gang

    2014-12-01

    Bag-of-features (BoFs) representation has been extensively applied to deal with various computer vision applications. To extract discriminative and descriptive BoF, one important step is to learn a good dictionary to minimize the quantization loss between local features and codewords. While most existing visual dictionary learning approaches are engaged with unsupervised feature quantization, the latest trend has turned to supervised learning by harnessing the semantic labels of images or regions. However, such labels are typically too expensive to acquire, which restricts the scalability of supervised dictionary learning approaches. In this paper, we propose to leverage image attributes to weakly supervise the dictionary learning procedure without requiring any actual labels. As a key contribution, our approach establishes a generative hidden Markov random field (HMRF), which models the quantized codewords as the observed states and the image attributes as the hidden states, respectively. Dictionary learning is then performed by supervised grouping the observed states, where the supervised information is stemmed from the hidden states of the HMRF. In such a way, the proposed dictionary learning approach incorporates the image attributes to learn a semantic-preserving BoF representation without any genuine supervision. Experiments in large-scale image retrieval and classification tasks corroborate that our approach significantly outperforms the state-of-the-art unsupervised dictionary learning approaches.

  3. A New Method for Solving Supervised Data Classification Problems

    Directory of Open Access Journals (Sweden)

    Parvaneh Shabanzadeh

    2014-01-01

    Full Text Available Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms.

  4. Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.

    Science.gov (United States)

    Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S; Pusey, Marc L; Aygün, Ramazan S

    2014-03-01

    In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset.

  5. A semi-supervised classification algorithm using the TAD-derived background as training data

    Science.gov (United States)

    Fan, Lei; Ambeau, Brittany; Messinger, David W.

    2013-05-01

    In general, spectral image classification algorithms fall into one of two categories: supervised and unsupervised. In unsupervised approaches, the algorithm automatically identifies clusters in the data without a priori information about those clusters (except perhaps the expected number of them). Supervised approaches require an analyst to identify training data to learn the characteristics of the clusters such that they can then classify all other pixels into one of the pre-defined groups. The classification algorithm presented here is a semi-supervised approach based on the Topological Anomaly Detection (TAD) algorithm. The TAD algorithm defines background components based on a mutual k-Nearest Neighbor graph model of the data, along with a spectral connected components analysis. Here, the largest components produced by TAD are used as regions of interest (ROI's),or training data for a supervised classification scheme. By combining those ROI's with a Gaussian Maximum Likelihood (GML) or a Minimum Distance to the Mean (MDM) algorithm, we are able to achieve a semi supervised classification method. We test this classification algorithm against data collected by the HyMAP sensor over the Cooke City, MT area and University of Pavia scene.

  6. QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms

    Directory of Open Access Journals (Sweden)

    Ardjan Zwartjes

    2016-10-01

    Full Text Available In this work, we introduce QUEST (QUantile Estimation after Supervised Training, an adaptive classification algorithm for Wireless Sensor Networks (WSNs that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

  7. QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms.

    Science.gov (United States)

    Zwartjes, Ardjan; Havinga, Paul J M; Smit, Gerard J M; Hurink, Johann L

    2016-10-01

    In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

  8. Active Metric Learning for Supervised Classification

    OpenAIRE

    Kumaran, Krishnan; Papageorgiou, Dimitri; Chang, Yutong; Li, Minhan; Takáč, Martin

    2018-01-01

    Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature. Additionally, we generalize and improve upon leading methods by removing reliance on pre-designated "target neighbors," "triplets," and "similarity pairs." Another salient feature of our method is its ability to en...

  9. A supervised learning rule for classification of spatiotemporal spike patterns.

    Science.gov (United States)

    Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

    2016-08-01

    This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

  10. Enhanced manifold regularization for semi-supervised classification.

    Science.gov (United States)

    Gan, Haitao; Luo, Zhizeng; Fan, Yingle; Sang, Nong

    2016-06-01

    Manifold regularization (MR) has become one of the most widely used approaches in the semi-supervised learning field. It has shown superiority by exploiting the local manifold structure of both labeled and unlabeled data. The manifold structure is modeled by constructing a Laplacian graph and then incorporated in learning through a smoothness regularization term. Hence the labels of labeled and unlabeled data vary smoothly along the geodesics on the manifold. However, MR has ignored the discriminative ability of the labeled and unlabeled data. To address the problem, we propose an enhanced MR framework for semi-supervised classification in which the local discriminative information of the labeled and unlabeled data is explicitly exploited. To make full use of labeled data, we firstly employ a semi-supervised clustering method to discover the underlying data space structure of the whole dataset. Then we construct a local discrimination graph to model the discriminative information of labeled and unlabeled data according to the discovered intrinsic structure. Therefore, the data points that may be from different clusters, though similar on the manifold, are enforced far away from each other. Finally, the discrimination graph is incorporated into the MR framework. In particular, we utilize semi-supervised fuzzy c-means and Laplacian regularized Kernel minimum squared error for semi-supervised clustering and classification, respectively. Experimental results on several benchmark datasets and face recognition demonstrate the effectiveness of our proposed method.

  11. Classification of perovskites with supervised self-organizing maps

    International Nuclear Information System (INIS)

    Kuzmanovski, Igor; Dimitrovska-Lazova, Sandra; Aleksovska, Slobotka

    2007-01-01

    In this work supervised self-organizing maps were used for structural classification of perovskites. For this purpose, structural data for total number of 286 perovskites, belonging to ABO 3 and/or A 2 BB'O 6 types, were collected from literature: 130 of these are cubic, 85 orthorhombic and 71 monoclinic. For classification purposes, the effective ionic radii of the cations, electronegativities of the cations in B-position, as well as, the oxidation states of these cations, were used as input variables. The parameters of the developed models, as well as, the most suitable variables for classification purposes were selected using genetic algorithms. Two-third of all the compounds were used in the training phase. During the optimization process the performances of the models were checked using cross-validation leave-1/10-out. The performances of obtained solutions were checked using the test set composed of the remaining one-third of the compounds. The obtained models for classification of these three classes of perovskite compounds show very good results. Namely, the classification of the compounds in the test set resulted in small number of discrepancies (4.2-6.4%) between the actual crystallographic class and the one predicted by the models. All these results are strong arguments for the validity of supervised self-organizing maps for performing such types of classification. Therefore, the proposed procedure could be successfully used for crystallographic classification of perovskites in one of these three classes

  12. Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning.

    Science.gov (United States)

    Gönen, Mehmet

    2014-03-01

    Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F 1 , and micro F 1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks.

  13. Robust Semi-Supervised Manifold Learning Algorithm for Classification

    Directory of Open Access Journals (Sweden)

    Mingxia Chen

    2018-01-01

    Full Text Available In the recent years, manifold learning methods have been widely used in data classification to tackle the curse of dimensionality problem, since they can discover the potential intrinsic low-dimensional structures of the high-dimensional data. Given partially labeled data, the semi-supervised manifold learning algorithms are proposed to predict the labels of the unlabeled points, taking into account label information. However, these semi-supervised manifold learning algorithms are not robust against noisy points, especially when the labeled data contain noise. In this paper, we propose a framework for robust semi-supervised manifold learning (RSSML to address this problem. The noisy levels of the labeled points are firstly predicted, and then a regularization term is constructed to reduce the impact of labeled points containing noise. A new robust semi-supervised optimization model is proposed by adding the regularization term to the traditional semi-supervised optimization model. Numerical experiments are given to show the improvement and efficiency of RSSML on noisy data sets.

  14. Semi-supervised morphosyntactic classification of Old Icelandic.

    Science.gov (United States)

    Urban, Kryztof; Tangherlini, Timothy R; Vijūnas, Aurelijus; Broadwell, Peter M

    2014-01-01

    We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries. A web-based GUI allows expert users to modify and augment data through an online process. A machine learning module incorporates prototype data, edit-distance metrics, and expert feedback to continuously update part-of-speech and morphosyntactic classification. An advantage of the analyzer is its ability to achieve competitive classification accuracy with minimum training data.

  15. Classification in Medical Imaging

    DEFF Research Database (Denmark)

    Chen, Chen

    Classification is extensively used in the context of medical image analysis for the purpose of diagnosis or prognosis. In order to classify image content correctly, one needs to extract efficient features with discriminative properties and build classifiers based on these features. In addition...... on characterizing human faces and emphysema disease in lung CT images....

  16. Improving the potential of pixel-based supervised classification in ...

    African Journals Online (AJOL)

    The goal of this paper was to describe the impact of various parameters when applying a supervised Maximum Likelihood Classifier (MLC) to SPOT 5 image analysis in a remote savanna biome. Pair separation indicators and probability thresholds were used to analyse the effect of training area size and heterogeneity as ...

  17. Cellular image classification

    CERN Document Server

    Xu, Xiang; Lin, Feng

    2017-01-01

    This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical...

  18. Learning Supervised Topic Models for Classification and Regression from Crowds.

    Science.gov (United States)

    Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete; Pereira, Francisco C

    2017-12-01

    The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.

  19. Supervised and Unsupervised Classification for Pattern Recognition Purposes

    Directory of Open Access Journals (Sweden)

    Catalina COCIANU

    2006-01-01

    Full Text Available A cluster analysis task has to identify the grouping trends of data, to decide on the sound clusters as well as to validate somehow the resulted structure. The identification of the grouping tendency existing in a data collection assumes the selection of a framework stated in terms of a mathematical model allowing to express the similarity degree between couples of particular objects, quasi-metrics expressing the similarity between an object an a cluster and between clusters, respectively. In supervised classification, we are provided with a collection of preclassified patterns, and the problem is to label a newly encountered pattern. Typically, the given training patterns are used to learn the descriptions of classes which in turn are used to label a new pattern. The final section of the paper presents a new methodology for supervised learning based on PCA. The classes are represented in the measurement/feature space by a continuous repartitions

  20. Classification of iconic images

    OpenAIRE

    Zrianina, Mariia; Kopf, Stephan

    2016-01-01

    Iconic images represent an abstract topic and use a presentation that is intuitively understood within a certain cultural context. For example, the abstract topic “global warming” may be represented by a polar bear standing alone on an ice floe. Such images are widely used in media and their automatic classification can help to identify high-level semantic concepts. This paper presents a system for the classification of iconic images. It uses a variation of the Bag of Visual Words approach wi...

  1. Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

    Directory of Open Access Journals (Sweden)

    Peder Heiselberg

    2017-11-01

    Full Text Available The European Space Agency Sentinel-2 satellites provide multispectral images with pixel sizes down to 10 m. This high resolution allows for fast and frequent detection, classification and discrimination of various objects in the sea, which is relevant in general and specifically for the vast Arctic environment. We analyze several sets of multispectral image data from Denmark and Greenland fall and winter, and describe a supervised search and classification algorithm based on physical parameters that successfully finds and classifies all objects in the sea with reflectance above a threshold. It discriminates between objects like ships, islands, wakes, and icebergs, ice floes, and clouds with accuracy better than 90%. Pan-sharpening the infrared bands leads to classification and discrimination of ice floes and clouds better than 95%. For complex images with abundant ice floes or clouds, however, the false alarm rate dominates for small non-sailing boats.

  2. Learning Supervised Topic Models for Classification and Regression from Crowds

    DEFF Research Database (Denmark)

    Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete

    2017-01-01

    problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages...... annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression...

  3. Out-of-Sample Generalizations for Supervised Manifold Learning for Classification.

    Science.gov (United States)

    Vural, Elif; Guillemot, Christine

    2016-03-01

    Supervised manifold learning methods for data classification map high-dimensional data samples to a lower dimensional domain in a structure-preserving way while increasing the separation between different classes. Most manifold learning methods compute the embedding only of the initially available data; however, the generalization of the embedding to novel points, i.e., the out-of-sample extension problem, becomes especially important in classification applications. In this paper, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with an iterative process. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.

  4. Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

    KAUST Repository

    Wang, Jingbin

    2015-10-09

    In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., An image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods.

  5. Semi-Supervised Learning for Classification of Protein Sequence Data

    Directory of Open Access Journals (Sweden)

    Brian R. King

    2008-01-01

    Full Text Available Protein sequence data continue to become available at an exponential rate. Annotation of functional and structural attributes of these data lags far behind, with only a small fraction of the data understood and labeled by experimental methods. Classification methods that are based on semi-supervised learning can increase the overall accuracy of classifying partly labeled data in many domains, but very few methods exist that have shown their effect on protein sequence classification. We show how proven methods from text classification can be applied to protein sequence data, as we consider both existing and novel extensions to the basic methods, and demonstrate restrictions and differences that must be considered. We demonstrate comparative results against the transductive support vector machine, and show superior results on the most difficult classification problems. Our results show that large repositories of unlabeled protein sequence data can indeed be used to improve predictive performance, particularly in situations where there are fewer labeled protein sequences available, and/or the data are highly unbalanced in nature.

  6. Active relearning for robust supervised classification of pulmonary emphysema

    Science.gov (United States)

    Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

    2012-03-01

    Radiologists are adept at recognizing the appearance of lung parenchymal abnormalities in CT scans. However, the inconsistent differential diagnosis, due to subjective aggregation, mandates supervised classification. Towards optimizing Emphysema classification, we introduce a physician-in-the-loop feedback approach in order to minimize uncertainty in the selected training samples. Using multi-view inductive learning with the training samples, an ensemble of Support Vector Machine (SVM) models, each based on a specific pair-wise dissimilarity metric, was constructed in less than six seconds. In the active relearning phase, the ensemble-expert label conflicts were resolved by an expert. This just-in-time feedback with unoptimized SVMs yielded 15% increase in classification accuracy and 25% reduction in the number of support vectors. The generality of relearning was assessed in the optimized parameter space of six different classifiers across seven dissimilarity metrics. The resultant average accuracy improved to 21%. The co-operative feedback method proposed here could enhance both diagnostic and staging throughput efficiency in chest radiology practice.

  7. Supervised classification of distributed data streams for smart grids

    Energy Technology Data Exchange (ETDEWEB)

    Guarracino, Mario R. [High Performance Computing and Networking - National Research Council of Italy, Naples (Italy); Irpino, Antonio; Verde, Rosanna [Seconda Universita degli Studi di Napoli, Dipartimento di Studi Europei e Mediterranei, Caserta (Italy); Radziukyniene, Neringa [Lithuanian Energy Institute, Laboratory of Systems Control and Automation, Kaunas (Lithuania)

    2012-03-15

    The electricity system inherited from the 19th and 20th centuries has been a reliable but centralized system. With the spreading of local, distributed and intermittent renewable energy resources, top-down central control of the grid no longer meets modern requirements. For these reasons, the power grid has been equipped with smart meters integrating bi-directional communications, advanced power measurement and management capabilities. Smart meters make it possible to remotely turn power on or off to a customer, read usage information, detect a service outage and the unauthorized use of electricity. To fully exploit their capabilities, we foresee the usage of distributed supervised classification algorithms. By gathering data available from meters and other sensors, such algorithms can create local classification models for attack detection, online monitoring, privacy preservation, workload balancing, prediction of energy demand and incoming faults. In this paper we present a decentralized distributed classification algorithm based on proximal support vector machines. The method uses partial knowledge, in form of data streams, to build its local model on each meter. We demonstrate the performance of the proposed scheme on synthetic datasets. (orig.)

  8. Multispectral Image classification using the theories of neural networks

    International Nuclear Information System (INIS)

    Ardisasmita, M.S.; Subki, M.I.R.

    1997-01-01

    Image classification is the one of the important part of digital image analysis. the objective of image classification is to identify and regroup the features occurring in an image into one or several classes in terms of the object. basic to the understanding of multispectral classification is the concept of the spectral response of an object as a function of the electromagnetic radiation and the wavelength of the spectrum. new approaches to classification has been developed to improve the result of analysis, these state-of-the-art classifiers are based upon the theories of neural networks. Neural network classifiers are algorithmes which mimic the computational abilities of the human brain. Artificial neurons are simple emulation's of biological neurons; they take in information from sensors or other artificial neurons, perform very simple operations on this data, and pass the result to other recognize the spectral signature of each image pixel. Neural network image classification has been divided into supervised and unsupervised training procedures. In the supervised approach, examples of each cover type can be located and the computer can compute spectral signatures to categorize all pixels in a digital image into several land cover classes. In supervised classification, spectral signatures are generated by mathematically grouping and it does not require analyst-specified training data. Thus, in the supervised approach we define useful information categories and then examine their spectral reparability; in the unsupervised approach the computer determines spectrally sapable classes and then we define thei information value

  9. Image Classification Workflow Using Machine Learning Methods

    Science.gov (United States)

    Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.

    2016-12-01

    Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.

  10. Supervised Learning Applied to Air Traffic Trajectory Classification

    Science.gov (United States)

    Bosson, Christabelle; Nikoleris, Tasos

    2018-01-01

    Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

  11. Supervised machine learning and active learning in classification of radiology reports.

    Science.gov (United States)

    Nguyen, Dung H M; Patrick, Jon D

    2014-01-01

    This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry. In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney). The reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL. AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly. The most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  12. Establishing a Supervised Classification of Global Blue Carbon Mangrove Ecosystems

    Science.gov (United States)

    Baltezar, P.

    2016-12-01

    Understanding change in mangroves over time will aid forest management systems working to protect them from over exploitation. Mangroves are one of the most carbon dense terrestrial ecosystems on the planet and are therefore a high priority for sustainable forest management. Although they represent 1% of terrestrial cover, they could account for about 10% of global carbon emissions. The foundation of this analysis uses remote sensing to establish a supervised classification of mangrove forests for discrete regions in the Zambezi Delta of Mozambique and the Rufiji Delta of Tanzania. Open-source mapping platforms provided a dynamic space for analyzing satellite imagery in the Google Earth Engine (GEE) coding environment. C-Band Synthetic Aperture Radar data from Sentinel 1 was used in the model as a mask by optimizing SAR parameters. Exclusion metrics identified within Global Land Surface Temperature data from MODIS and the Shuttle Radar Topography Mission were used to accentuate mangrove features. Variance was accounted for in exclusion metrics by statistically calculating thresholds for radar, thermal, and elevation data. Optical imagery from the Landsat 8 archive aided a quality mosaic in extracting the highest spectral index values most appropriate for vegetative mapping. The enhanced radar, thermal, and digital elevation imagery were then incorporated into the quality mosaic. Training sites were selected from Google Earth imagery and used in the classification with a resulting output of four mangrove cover map models for each site. The model was assessed for accuracy by observing the differences between the mangrove classification models to the reference maps. Although the model was over predicting mangroves in non-mangrove regions, it was more accurately classifying mangrove regions established by the references. Future refinements will expand the model with an objective degree of accuracy.

  13. Nonparametric Mixture Models for Supervised Image Parcellation.

    Science.gov (United States)

    Sabuncu, Mert R; Yeo, B T Thomas; Van Leemput, Koen; Fischl, Bruce; Golland, Polina

    2009-09-01

    We present a nonparametric, probabilistic mixture model for the supervised parcellation of images. The proposed model yields segmentation algorithms conceptually similar to the recently developed label fusion methods, which register a new image with each training image separately. Segmentation is achieved via the fusion of transferred manual labels. We show that in our framework various settings of a model parameter yield algorithms that use image intensity information differently in determining the weight of a training subject during fusion. One particular setting computes a single, global weight per training subject, whereas another setting uses locally varying weights when fusing the training data. The proposed nonparametric parcellation approach capitalizes on recently developed fast and robust pairwise image alignment tools. The use of multiple registrations allows the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and subcortical structures. The results demonstrate that the proposed nonparametric segmentation framework yields significantly better segmentation than state-of-the-art algorithms.

  14. Supervised learning for the automated transcription of spacer classification from spoligotype films

    Directory of Open Access Journals (Sweden)

    Abernethy Neil

    2009-08-01

    Full Text Available Abstract Background Molecular genotyping of bacteria has revolutionized the study of tuberculosis epidemiology, yet these established laboratory techniques typically require subjective and laborious interpretation by trained professionals. In the context of a Tuberculosis Case Contact study in The Gambia we used a reverse hybridization laboratory assay called spoligotype analysis. To facilitate processing of spoligotype images we have developed tools and algorithms to automate the classification and transcription of these data directly to a database while allowing for manual editing. Results Features extracted from each of the 1849 spots on a spoligo film were classified using two supervised learning algorithms. A graphical user interface allows manual editing of the classification, before export to a database. The application was tested on ten films of differing quality and the results of the best classifier were compared to expert manual classification, giving a median correct classification rate of 98.1% (inter quartile range: 97.1% to 99.2%, with an automated processing time of less than 1 minute per film. Conclusion The software implementation offers considerable time savings over manual processing whilst allowing expert editing of the automated classification. The automatic upload of the classification to a database reduces the chances of transcription errors.

  15. Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data

    Science.gov (United States)

    Churches, Christopher E.; Wampler, Peter J.; Sun, Wanxiao; Smith, Andrew J.

    2014-08-01

    This study uses 2010-2011 Landsat Thematic Mapper (TM) imagery to estimate total forested area in Haiti. The thematic map was generated using radiometric normalization of digital numbers by a modified normalization method utilizing pseudo-invariant polygons (PIPs), followed by supervised classification of the mosaicked image using the Food and Agriculture Organization (FAO) of the United Nations Land Cover Classification System. Classification results were compared to other sources of land-cover data produced for similar years, with an emphasis on the statistics presented by the FAO. Three global land cover datasets (GLC2000, Globcover, 2009, and MODIS MCD12Q1), and a national-scale dataset (a land cover analysis by Haitian National Centre for Geospatial Information (CNIGS)) were reclassified and compared. According to our classification, approximately 32.3% of Haiti's total land area was tree covered in 2010-2011. This result was confirmed using an error-adjusted area estimator, which predicted a tree covered area of 32.4%. Standardization to the FAO's forest cover class definition reduces the amount of tree cover of our supervised classification to 29.4%. This result was greater than the reported FAO value of 4% and the value for the recoded GLC2000 dataset of 7.0%, but is comparable to values for three other recoded datasets: MCD12Q1 (21.1%), Globcover (2009) (26.9%), and CNIGS (19.5%). We propose that at coarse resolutions, the segmented and patchy nature of Haiti's forests resulted in a systematic underestimation of the extent of forest cover. It appears the best explanation for the significant difference between our results, FAO statistics, and compared datasets is the accuracy of the data sources and the resolution of the imagery used for land cover analyses. Analysis of recoded global datasets and results from this study suggest a strong linear relationship (R2 = 0.996 for tree cover) between spatial resolution and land cover estimates.

  16. MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING

    Data.gov (United States)

    National Aeronautics and Space Administration — MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING MOHAMMAD SALIM AHMED, LATIFUR KHAN, NIKUNJ OZA, AND MANDAVA RAJESWARI Abstract....

  17. Toward Determination of Venous Thrombosis Ages by Using Fuzzy Logic and Supervised Bayes Classification

    National Research Council Canada - National Science Library

    Lim, P

    2001-01-01

    .... Thus, the proposed learning base is constructed in a 3-tuple: observation, label, membership value in term of fuzzy logic for each class and not a 2-tuple as in the usual supervised Bayes classification application...

  18. Automatic Classification Using Supervised Learning in a Medical Document Filtering Application.

    Science.gov (United States)

    Mostafa, J.; Lam, W.

    2000-01-01

    Presents a multilevel model of the information filtering process that permits document classification. Evaluates a document classification approach based on a supervised learning algorithm, measures the accuracy of the algorithm in a neural network that was trained to classify medical documents on cell biology, and discusses filtering…

  19. The Costs of Supervised Classification: The Effect of Learning Task on Conceptual Flexibility

    Science.gov (United States)

    Hoffman, Aaron B.; Rehder, Bob

    2010-01-01

    Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features, whereas learning by inferring missing features promotes the acquisition of within-category information. Accordingly, we predicted that classification learning would produce a deficit in people's ability to draw "novel…

  20. Constrained Deep Weak Supervision for Histopathology Image Segmentation.

    Science.gov (United States)

    Jia, Zhipeng; Huang, Xingyi; Chang, Eric I-Chao; Xu, Yan

    2017-11-01

    In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive instances are introduced in our approach to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates the state-of-the-art results on large-scale histopathology image data sets and can be applied to various applications in medical imaging beyond histopathology images, such as MRI, CT, and ultrasound images.

  1. Semi-supervised Probabilistic Distance Clustering and the Uncertainty of Classification

    Science.gov (United States)

    Iyigun, Cem; Ben-Israel, Adi

    Semi-supervised clustering is an attempt to reconcile clustering (unsupervised learning) and classification (supervised learning, using prior information on the data). These two modes of data analysis are combined in a parameterized model, the parameter θ ∈ [0, 1] is the weight attributed to the prior information, θ = 0 corresponding to clustering, and θ = 1 to classification. The results (cluster centers, classification rule) depend on the parameter θ, an insensitivity to θ indicates that the prior information is in agreement with the intrinsic cluster structure, and is otherwise redundant. This explains why some data sets (such as the Wisconsin breast cancer data, Merz and Murphy, UCI repository of machine learning databases, University of California, Irvine, CA) give good results for all reasonable classification methods. The uncertainty of classification is represented here by the geometric mean of the membership probabilities, shown to be an entropic distance related to the Kullback-Leibler divergence.

  2. A Supervised Classification Algorithm for Note Onset Detection

    Directory of Open Access Journals (Sweden)

    Douglas Eck

    2007-01-01

    Full Text Available This paper presents a novel approach to detecting onsets in music audio files. We use a supervised learning algorithm to classify spectrogram frames extracted from digital audio as being onsets or nononsets. Frames classified as onsets are then treated with a simple peak-picking algorithm based on a moving average. We present two versions of this approach. The first version uses a single neural network classifier. The second version combines the predictions of several networks trained using different hyperparameters. We describe the details of the algorithm and summarize the performance of both variants on several datasets. We also examine our choice of hyperparameters by describing results of cross-validation experiments done on a custom dataset. We conclude that a supervised learning approach to note onset detection performs well and warrants further investigation.

  3. 7 CFR 27.80 - Fees; classification, Micronaire, and supervision.

    Science.gov (United States)

    2010-01-01

    ....80 Section 27.80 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards, Inspections, Marketing Practices), DEPARTMENT OF AGRICULTURE COMMODITY STANDARDS AND STANDARD CONTAINER REGULATIONS COTTON CLASSIFICATION UNDER COTTON FUTURES LEGISLATION Regulations Costs of...

  4. Supervised Classification Processes for the Characterization of Heritage Elements, Case Study: Cuenca-Ecuador

    Science.gov (United States)

    Briones, J. C.; Heras, V.; Abril, C.; Sinchi, E.

    2017-08-01

    The proper control of built heritage entails many challenges related to the complexity of heritage elements and the extent of the area to be managed, for which the available resources must be efficiently used. In this scenario, the preventive conservation approach, based on the concept that prevent is better than cure, emerges as a strategy to avoid the progressive and imminent loss of monuments and heritage sites. Regular monitoring appears as a key tool to identify timely changes in heritage assets. This research demonstrates that the supervised learning model (Support Vector Machines - SVM) is an ideal tool that supports the monitoring process detecting visible elements in aerial images such as roofs structures, vegetation and pavements. The linear, gaussian and polynomial kernel functions were tested; the lineal function provided better results over the other functions. It is important to mention that due to the high level of segmentation generated by the classification procedure, it was necessary to apply a generalization process through opening a mathematical morphological operation, which simplified the over classification for the monitored elements.

  5. Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

    International Nuclear Information System (INIS)

    Korfiatis, P; Costaridou, L; Kalogeropoulou, C; Petsas, T; Daoussis, D; Adonopoulos, A

    2009-01-01

    Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

  6. Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

    Science.gov (United States)

    Korfiatis, P.; Kalogeropoulou, C.; Daoussis, D.; Petsas, T.; Adonopoulos, A.; Costaridou, L.

    2009-07-01

    Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

  7. AUTOMATIC APPROACH TO VHR SATELLITE IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    P. Kupidura

    2016-06-01

    Full Text Available In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture, which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the

  8. Artificial neural net system for interactive tissue classification with MR imaging and image segmentation

    International Nuclear Information System (INIS)

    Clarke, L.P.; Silbiger, M.; Naylor, C.; Brown, K.

    1990-01-01

    This paper reports on the development of interactive methods for MR tissue classification that permit mathematically rigorous methods for three-dimensional image segmentation and automatic organ/tumor contouring, as required for surgical and RTP planning. The authors investigate a number of image-intensity based tissue- classification methods that make no implicit assumptions on the MR parameters and hence are not limited by image data set. Similarly, we have trained artificial neural net (ANN) systems for both supervised and unsupervised tissue classification

  9. Automated supervised classification of variable stars. I. Methodology

    NARCIS (Netherlands)

    Debosscher, J.; Sarro, L.M.; Aerts, C.C.; Cuypers, J.; Vandenbussche, B.; Garrido, R.; Solano, E.

    2007-01-01

    Context: The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the classes in terms of physical parameters is also

  10. Benchmarking protein classification algorithms via supervised cross-validation

    NARCIS (Netherlands)

    Kertész-Farkas, A.; Dhir, S.; Sonego, P.; Pacurar, M.; Netoteia, S.; Nijveen, H.; Kuzniar, A.; Leunissen, J.A.M.; Kocsor, A.; Pongor, S.

    2008-01-01

    Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold,

  11. Effects of supervised Self Organising Maps parameters on classification performance.

    Science.gov (United States)

    Ballabio, Davide; Vasighi, Mahdi; Filzmoser, Peter

    2013-02-26

    Self Organising Maps (SOMs) are one of the most powerful learning strategies among neural networks algorithms. SOMs have several adaptable parameters and the selection of appropriate network architectures is required in order to make accurate predictions. The major disadvantage of SOMs is probably due to the network optimisation, since this procedure can be often time-expensive. Effects of network size, training epochs and learning rate on the classification performance of SOMs are known, whereas the effect of other parameters (type of SOMs, weights initialisation, training algorithm, topology and boundary conditions) are not so obvious. This study was addressed to analyse the effect of SOMs parameters on the network classification performance, as well as on their computational times, taking into consideration a significant number of real datasets, in order to achieve a comprehensive statistical comparison. Parameters were contemporaneously evaluated by means of an approach based on the design of experiments, which enabled the investigation of their interaction effects. Results highlighted the most important parameters which influence the classification performance and enabled the identification of the optimal settings, as well as the optimal architectures to reduce the computational time of SOMs. Copyright © 2012 Elsevier B.V. All rights reserved.

  12. GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

    Directory of Open Access Journals (Sweden)

    Lintao Yang

    2018-01-01

    Full Text Available With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH technology. The proposed algorithm consists of three main stages: (1 training the basic classifier; (2 selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3 training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection and GMDH-U (GMDH-based semi-supervised feature selection for customer classification models.

  13. Deep learning for image classification

    Science.gov (United States)

    McCoppin, Ryan; Rizki, Mateen

    2014-06-01

    This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

  14. Supervised classification of combined copy number and gene expression data

    Directory of Open Access Journals (Sweden)

    Riccadonna S.

    2007-12-01

    Full Text Available In this paper we apply a predictive profiling method to genome copy number aberrations (CNA in combination with gene expression and clinical data to identify molecular patterns of cancer pathophysiology. Predictive models and optimal feature lists for the platforms are developed by a complete validation SVM-based machine learning system. Ranked list of genome CNA sites (assessed by comparative genomic hybridization arrays – aCGH and of differentially expressed genes (assessed by microarray profiling with Affy HG-U133A chips are computed and combined on a breast cancer dataset for the discrimination of Luminal/ ER+ (Lum/ER+ and Basal-like/ER- classes. Different encodings are developed and applied to the CNA data, and predictive variable selection is discussed. We analyze the combination of profiling information between the platforms, also considering the pathophysiological data. A specific subset of patients is identified that has a different response to classification by chromosomal gains and losses and by differentially expressed genes, corroborating the idea that genomic CNA can represent an independent source for tumor classification.

  15. An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species

    Directory of Open Access Journals (Sweden)

    Deborah Galpert

    2015-01-01

    Full Text Available Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification.

  16. Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

    Science.gov (United States)

    Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

    2015-01-01

    Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

  17. Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

    Science.gov (United States)

    Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

    2017-01-01

    Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data. PMID:28403159

  18. Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

    KAUST Repository

    Wang, Jingbin; Zhou, Yihua; Duan, Kanghong; Wang, Jim Jing-Yan; Bensmail, Halima

    2015-01-01

    . In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor

  19. Dynamic classification system in large-scale supervision of energy efficiency in buildings

    International Nuclear Information System (INIS)

    Kiluk, S.

    2014-01-01

    Highlights: • Rough set approximation of classification improves energy efficiency prediction. • Dynamic features of diagnostic classification allow for its precise prediction. • Indiscernibility in large population enhances identification of process features. • Diagnostic information can be refined by dynamic references to local neighbourhood. • We introduce data exploration validation based on system dynamics and uncertainty. - Abstract: Data mining and knowledge discovery applied to the billing data provide the diagnostic instruments for the evaluation of energy use in buildings connected to a district heating network. To ensure the validity of an algorithm-based classification system, the dynamic properties of a sequence of partitions for consecutive detected events were investigated. The information regarding the dynamic properties of the classification system refers to the similarities between the supervised objects and migrations that originate from the changes in the building energy use and loss similarity to their neighbourhood and thus represents the refinement of knowledge. In this study, we demonstrate that algorithm-based diagnostic knowledge has dynamic properties that can be exploited with a rough set predictor to evaluate whether the implementation of classification for supervision of energy use aligns with the dynamics of changes of district heating-supplied building properties. Moreover, we demonstrate the refinement of the current knowledge with the previous findings and we present the creation of predictive diagnostic systems based on knowledge dynamics with a satisfactory level of classification errors, even for non-stationary data

  20. Remote Sensing Image Classification Based on Stacked Denoising Autoencoder

    Directory of Open Access Journals (Sweden)

    Peng Liang

    2017-12-01

    Full Text Available Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing, characteristics are obtained in supervised learning by Back Propagation (BP neural network, and the whole network is optimized by error back propagation. Finally, Gaofen-1 satellite (GF-1 remote sensing data are used for evaluation, and the total accuracy and kappa accuracy reach 95.7% and 0.955, respectively, which are higher than that of the Support Vector Machine and Back Propagation neural network. The experiment results show that the proposed method can effectively improve the accuracy of remote sensing image classification.

  1. Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Zhiling Guo

    2016-03-01

    Full Text Available In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost and convolutional neural networks (CNN. To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.

  2. Weighting training images by maximizing distribution similarity for supervised segmentation across scanners

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Vernooij, Meike W; Ikram, M.Arfan

    2015-01-01

    Many automatic segmentation methods are based on supervised machine learning. Such methods have proven to perform well, on the condition that they are trained on a sufficiently large manually labeled training set that is representative of the images to segment. However, due to differences between...... scanners, scanning parameters, and patients such a training set may be difficult to obtain. We present a transfer-learning approach to segmentation by multi-feature voxelwise classification. The presented method can be trained using a heterogeneous set of training images that may be obtained with different...... scanners than the target image. In our approach each training image is given a weight based on the distribution of its voxels in the feature space. These image weights are chosen as to minimize the difference between the weighted probability density function (PDF) of the voxels of the training images...

  3. SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    T. Hamsapriya

    2011-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

  4. Musical Instrument Classification Based on Nonlinear Recurrence Analysis and Supervised Learning

    Directory of Open Access Journals (Sweden)

    R.Rui

    2013-04-01

    Full Text Available In this paper, the phase space reconstruction of time series produced by different instruments is discussed based on the nonlinear dynamic theory. The dense ratio, a novel quantitative recurrence parameter, is proposed to describe the difference of wind instruments, stringed instruments and keyboard instruments in the phase space by analyzing the recursive property of every instrument. Furthermore, a novel supervised learning algorithm for automatic classification of individual musical instrument signals is addressed deriving from the idea of supervised non-negative matrix factorization (NMF algorithm. In our approach, the orthogonal basis matrix could be obtained without updating the matrix iteratively, which NMF is unable to do. The experimental results indicate that the accuracy of the proposed method is improved by 3% comparing with the conventional features in the individual instrument classification.

  5. Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.

    Science.gov (United States)

    Xu, Jiucheng; Mu, Huiyu; Wang, Yun; Huang, Fangzhou

    2018-01-01

    The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC 2 ), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

  6. Classification and Weakly Supervised Pain Localization using Multiple Segment Representation.

    Science.gov (United States)

    Sikka, Karan; Dhall, Abhinav; Bartlett, Marian Stewart

    2014-10-01

    Automatic pain recognition from videos is a vital clinical application and, owing to its spontaneous nature, poses interesting challenges to automatic facial expression recognition (AFER) research. Previous pain vs no-pain systems have highlighted two major challenges: (1) ground truth is provided for the sequence, but the presence or absence of the target expression for a given frame is unknown, and (2) the time point and the duration of the pain expression event(s) in each video are unknown. To address these issues we propose a novel framework (referred to as MS-MIL) where each sequence is represented as a bag containing multiple segments, and multiple instance learning (MIL) is employed to handle this weakly labeled data in the form of sequence level ground-truth. These segments are generated via multiple clustering of a sequence or running a multi-scale temporal scanning window, and are represented using a state-of-the-art Bag of Words (BoW) representation. This work extends the idea of detecting facial expressions through 'concept frames' to 'concept segments' and argues through extensive experiments that algorithms such as MIL are needed to reap the benefits of such representation. The key advantages of our approach are: (1) joint detection and localization of painful frames using only sequence-level ground-truth, (2) incorporation of temporal dynamics by representing the data not as individual frames but as segments, and (3) extraction of multiple segments, which is well suited to signals with uncertain temporal location and duration in the video. Extensive experiments on UNBC-McMaster Shoulder Pain dataset highlight the effectiveness of the approach by achieving competitive results on both tasks of pain classification and localization in videos. We also empirically evaluate the contributions of different components of MS-MIL. The paper also includes the visualization of discriminative facial patches, important for pain detection, as discovered by our

  7. Classification of gene expression data: A hubness-aware semi-supervised approach.

    Science.gov (United States)

    Buza, Krisztian

    2016-04-01

    Classification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of unlabeled data. Gene expression data is high-dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers have been developed recently, such as Naive Hubness-Bayesian k-Nearest Neighbor (NHBNN). In this paper, we propose a semi-supervised extension of NHBNN which follows the self-training schema. As one of the core components of self-training is the certainty score, we propose a new hubness-aware certainty score. We performed experiments on publicly available gene expression data. These experiments show that the proposed classifier outperforms its competitors. We investigated the impact of each of the components (classification algorithm, semi-supervised technique, hubness-aware certainty score) separately and showed that each of these components are relevant to the performance of the proposed approach. Our results imply that our approach may increase classification accuracy and reduce computational costs (i.e., runtime). Based on the promising results presented in the paper, we envision that hubness-aware techniques will be used in various other biomedical machine learning tasks. In order to accelerate this process, we made an implementation of hubness-aware machine learning techniques publicly available in the PyHubs software package (http://www.biointelligence.hu/pyhubs) implemented in Python, one of the most popular programming languages of data science. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  8. Semi-Supervised Classification for Fault Diagnosis in Nuclear Power Plants

    International Nuclear Information System (INIS)

    Ma, Jian Ping; Jiang, Jin

    2014-01-01

    Pattern classification methods have become important tools for fault diagnosis in industrial systems. However, it is normally difficult to obtain reliable labeled data to train a supervised pattern classification model for applications in a nuclear power plant (NPP). However, unlabeled data easily become available through increased deployment of supervisory, control, and data acquisition (SCADA) systems. In this paper, a fault diagnosis scheme based on semi-supervised classification (SSC) method is developed with specific applications for NPP. In this scheme, newly measured plant data are treated as unlabeled data. They are integrated with selected labeled data to train a SSC model which is then used to estimate labels of the new data. Compared to exclusive supervised approaches, the proposed scheme requires significantly less number of labeled data to train a classifier. Furthermore, it is shown that higher degree of uncertainties in the labeled data can be tolerated. The developed scheme has been validated using the data generated from a desktop NPP simulator and also from a physical NPP simulator using a graph-based SSC algorithm. Two case studies have been used in the validation process. In the first case study, three faults have been simulated on the desktop simulator. These faults have all been classified successfully with only four labeled data points per fault case. In the second case, six types of fault are simulated on the physical NPP simulator. All faults have been successfully diagnosed. The results have demonstrated that SSC is a promising tool for fault diagnosis

  9. Classification of high resolution satellite images

    OpenAIRE

    Karlsson, Anders

    2003-01-01

    In this thesis the Support Vector Machine (SVM)is applied on classification of high resolution satellite images. Sveral different measures for classification, including texture mesasures, 1st order statistics, and simple contextual information were evaluated. Additionnally, the image was segmented, using an enhanced watershed method, in order to improve the classification accuracy.

  10. Semi-supervised learning of hyperspectral image segmentation applied to vine tomatoes and table grapes

    Directory of Open Access Journals (Sweden)

    Jeroen van Roy

    2018-03-01

    Full Text Available Nowadays, quality inspection of fruit and vegetables is typically accomplished through visual inspection. Automation of this inspection is desirable to make it more objective. For this, hyperspectral imaging has been identified as a promising technique. When the field of view includes multiple objects, hypercubes should be segmented to assign individual pixels to different objects. Unsupervised and supervised methods have been proposed. While the latter are labour intensive as they require masking of the training images, the former are too computationally intensive for in-line use and may provide different results for different hypercubes. Therefore, a semi-supervised method is proposed to train a computationally efficient segmentation algorithm with minimal human interaction. As a first step, an unsupervised classification model is used to cluster spectra in similar groups. In the second step, a pixel selection algorithm applied to the output of the unsupervised classification is used to build a supervised model which is fast enough for in-line use. To evaluate this approach, it is applied to hypercubes of vine tomatoes and table grapes. After first derivative spectral preprocessing to remove intensity variation due to curvature and gloss effects, the unsupervised models segmented 86.11% of the vine tomato images correctly. Considering overall accuracy, sensitivity, specificity and time needed to segment one hypercube, partial least squares discriminant analysis (PLS-DA was found to be the best choice for in-line use, when using one training image. By adding a second image, the segmentation results improved considerably, yielding an overall accuracy of 96.95% for segmentation of vine tomatoes and 98.52% for segmentation of table grapes, demonstrating the added value of the learning phase in the algorithm.

  11. [Quantitative classification in catering trade and countermeasures of supervision and management in Hunan Province].

    Science.gov (United States)

    Liu, Xiulan; Chen, Lizhang; He, Xiang

    2012-02-01

    To analyze the status quo of quantitative classification in Hunan Province catering industry, and to discuss the countermeasures in-depth. According to relevant laws and regulations, and after referring to Daily supervision and quantitative scoring sheet and consulting experts, a checklist of key supervision indicators was made. The implementation of quantitative classification in 10 cities in Hunan Province was studied, and the status quo was analyzed. All the 390 catering units implemented quantitative classified management. The larger the catering enterprise, the higher level of quantitative classification. In addition to cafeterias, the smaller the catering units, the higher point of deduction, and snack bars and beverage stores were the highest. For those quantified and classified as C and D, the point of deduction was higher in the procurement and storage of raw materials, operation processing and other aspects. The quantitative classification of Hunan Province has relatively wide coverage. There are hidden risks in food security in small catering units, snack bars, and beverage stores. The food hygienic condition of Hunan Province needs to be improved.

  12. Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

    Directory of Open Access Journals (Sweden)

    Victoria Plaza-Leiva

    2017-03-01

    Full Text Available Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM, Gaussian processes (GP, and Gaussian mixture models (GMM. A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl. Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

  13. Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

    Science.gov (United States)

    Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

    2017-03-15

    Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

  14. Research on image evidence in land supervision and GIS management

    Science.gov (United States)

    Li, Qiu; Wu, Lixin

    2006-10-01

    Land resource development and utilization brings many problems. The numbers, the scale and volume of illegal land use cases are on the increasing. Since the territory is vast, and the land violations are concealment, it is difficulty for an effective land supervision and management. In this paper, the concepts of evidence, and preservation of evidence were described first. The concepts of image evidence (IE), natural evidence (NE), natural preservation of evidence (NPE), general preservation of evidence (GPE) were proposed based on the characteristics of remote sensing image (RSI) which has a characteristic of objectiveness, truthfulness, high spatial resolution, more information included. Using MapObjects and Visual Basic 6.0, under the Access management to implement the conjunction of spatial vector database and attribute data table; taking RSI as the data sources and background layer; combining the powerful management of geographic information system (GIS) for spatial data, and visual analysis, a land supervision and GIS management system was design and implemented based on NPE. The practical use in Beijing shows that the system is running well, and solved some problems in land supervision and management.

  15. Textural features for image classification

    Science.gov (United States)

    Haralick, R. M.; Dinstein, I.; Shanmugam, K.

    1973-01-01

    Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

  16. Supervised detection of exoplanets in high-contrast imaging sequences

    Science.gov (United States)

    Gomez Gonzalez, C. A.; Absil, O.; Van Droogenbroeck, M.

    2018-06-01

    Context. Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production of science-ready images relying on unsupervised learning techniques, such as low-rank approximations, for generating a model point spread function (PSF) and subtracting the residual starlight and speckle noise. Aims: In order to maximize the detection rate of HCI instruments and survey campaigns, advanced algorithms with higher sensitivities to faint companions are needed, especially for the speckle-dominated innermost region of the images. Methods: We propose a reformulation of the exoplanet detection task (for ADI sequences) that builds on well-established machine learning techniques to take HCI post-processing from an unsupervised to a supervised learning context. In this new framework, we present algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). We test these algorithms on real ADI datasets from VLT/NACO and VLT/SPHERE HCI instruments. We then assess their performances by injecting fake companions and using receiver operating characteristic analysis. This is done in comparison with state-of-the-art ADI algorithms, such as ADI principal component analysis (ADI-PCA). Results: This study shows the improved sensitivity versus specificity trade-off of the proposed supervised detection approach. At the diffraction limit, SODINN improves the true positive rate by a factor ranging from 2 to 10 (depending on the dataset and angular separation) with respect to ADI-PCA when working at the same false-positive level. Conclusions: The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. In addition, it offers the possibility of re-processing existing HCI databases to maximize their scientific return and potentially improve

  17. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

    Science.gov (United States)

    Jamal, Wasifa; Das, Saptarshi; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-08-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

  18. Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

    NARCIS (Netherlands)

    Cheplygina, Veronika; de Bruijne, Marleen; Pluim, Josien P. W.

    2018-01-01

    Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods which can learn

  19. Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

    Science.gov (United States)

    Matsubara, Takashi

    2017-01-01

    Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.

  20. Accuracy assessment between different image classification ...

    African Journals Online (AJOL)

    What image classification does is to assign pixel to a particular land cover and land use type that has the most similar spectral signature. However, there are possibilities that different methods or algorithms of image classification of the same data set could produce appreciable variant results in the sizes, shapes and areas of ...

  1. A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

    Science.gov (United States)

    Shyu, Mei-Ling; Sainani, Varsha

    The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.

  2. Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

    Science.gov (United States)

    Jiang, Yizhang; Wu, Dongrui; Deng, Zhaohong; Qian, Pengjiang; Wang, Jun; Wang, Guanjin; Chung, Fu-Lai; Choi, Kup-Sze; Wang, Shitong

    2017-12-01

    Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

  3. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Bin Hou

    2016-08-01

    Full Text Available Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD methods have been developed to solve them by utilizing remote sensing (RS images. The advent of high resolution (HR remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC segmentation. Then, saliency and morphological building index (MBI extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF. Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

  4. Supervised learning of tools for content-based search of image databases

    Science.gov (United States)

    Delanoy, Richard L.

    1996-03-01

    A computer environment, called the Toolkit for Image Mining (TIM), is being developed with the goal of enabling users with diverse interests and varied computer skills to create search tools for content-based image retrieval and other pattern matching tasks. Search tools are generated using a simple paradigm of supervised learning that is based on the user pointing at mistakes of classification made by the current search tool. As mistakes are identified, a learning algorithm uses the identified mistakes to build up a model of the user's intentions, construct a new search tool, apply the search tool to a test image, display the match results as feedback to the user, and accept new inputs from the user. Search tools are constructed in the form of functional templates, which are generalized matched filters capable of knowledge- based image processing. The ability of this system to learn the user's intentions from experience contrasts with other existing approaches to content-based image retrieval that base searches on the characteristics of a single input example or on a predefined and semantically- constrained textual query. Currently, TIM is capable of learning spectral and textural patterns, but should be adaptable to the learning of shapes, as well. Possible applications of TIM include not only content-based image retrieval, but also quantitative image analysis, the generation of metadata for annotating images, data prioritization or data reduction in bandwidth-limited situations, and the construction of components for larger, more complex computer vision algorithms.

  5. Objectness Supervised Merging Algorithm for Color Image Segmentation

    Directory of Open Access Journals (Sweden)

    Haifeng Sima

    2016-01-01

    Full Text Available Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

  6. Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

    Science.gov (United States)

    Han, Wenjing; Coutinho, Eduardo; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

    2016-01-01

    Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances. PMID:27627768

  7. Semi-supervised vibration-based classification and condition monitoring of compressors

    Science.gov (United States)

    Potočnik, Primož; Govekar, Edvard

    2017-09-01

    Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

  8. Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

    Science.gov (United States)

    Han, Wenjing; Coutinho, Eduardo; Ruan, Huabin; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

    2016-01-01

    Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances.

  9. Local Patch Vectors Encoded by Fisher Vectors for Image Classification

    Directory of Open Access Journals (Sweden)

    Shuangshuang Chen

    2018-02-01

    Full Text Available The objective of this work is image classification, whose purpose is to group images into corresponding semantic categories. Four contributions are made as follows: (i For computational simplicity and efficiency, we directly adopt raw image patch vectors as local descriptors encoded by Fisher vector (FV subsequently; (ii For obtaining representative local features within the FV encoding framework, we compare and analyze three typical sampling strategies: random sampling, saliency-based sampling and dense sampling; (iii In order to embed both global and local spatial information into local features, we construct an improved spatial geometry structure which shows good performance; (iv For reducing the storage and CPU costs of high dimensional vectors, we adopt a new feature selection method based on supervised mutual information (MI, which chooses features by an importance sorting algorithm. We report experimental results on dataset STL-10. It shows very promising performance with this simple and efficient framework compared to conventional methods.

  10. A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.

    Directory of Open Access Journals (Sweden)

    Fei Gao

    Full Text Available For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a "soft-start" approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.

  11. Prototype-based Models for the Supervised Learning of Classification Schemes

    Science.gov (United States)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2017-06-01

    An introduction is given to the use of prototype-based models in supervised machine learning. The main concept of the framework is to represent previously observed data in terms of so-called prototypes, which reflect typical properties of the data. Together with a suitable, discriminative distance or dissimilarity measure, prototypes can be used for the classification of complex, possibly high-dimensional data. We illustrate the framework in terms of the popular Learning Vector Quantization (LVQ). Most frequently, standard Euclidean distance is employed as a distance measure. We discuss how LVQ can be equipped with more general dissimilarites. Moreover, we introduce relevance learning as a tool for the data-driven optimization of parameterized distances.

  12. FROM2D to 3d Supervised Segmentation and Classification for Cultural Heritage Applications

    Science.gov (United States)

    Grilli, E.; Dininno, D.; Petrucci, G.; Remondino, F.

    2018-05-01

    The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials.

  13. Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification

    OpenAIRE

    Zhang, Chenrui; Peng, Yuxin

    2018-01-01

    Video representation learning is a vital problem for classification task. Recently, a promising unsupervised paradigm termed self-supervised learning has emerged, which explores inherent supervisory signals implied in massive data for feature learning via solving auxiliary tasks. However, existing methods in this regard suffer from two limitations when extended to video classification. First, they focus only on a single task, whereas ignoring complementarity among different task-specific feat...

  14. Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

    DEFF Research Database (Denmark)

    Heiselberg, Peder; Heiselberg, Henning

    2017-01-01

    . It discriminates between objects like ships, islands, wakes, and icebergs, ice floes, and clouds with accuracy better than 90%. Pan-sharpening the infrared bands leads to classification and discrimination of ice floes and clouds better than 95%. For complex images with abundant ice floes or clouds, however...

  15. SSC-EKE: Semi-Supervised Classification with Extensive Knowledge Exploitation.

    Science.gov (United States)

    Qian, Pengjiang; Xi, Chen; Xu, Min; Jiang, Yizhang; Su, Kuan-Hao; Wang, Shitong; Muzic, Raymond F

    2018-01-01

    We introduce a new, semi-supervised classification method that extensively exploits knowledge. The method has three steps. First, the manifold regularization mechanism, adapted from the Laplacian support vector machine (LapSVM), is adopted to mine the manifold structure embedded in all training data, especially in numerous label-unknown data. Meanwhile, by converting the labels into pairwise constraints, the pairwise constraint regularization formula (PCRF) is designed to compensate for the few but valuable labelled data. Second, by further combining the PCRF with the manifold regularization, the precise manifold and pairwise constraint jointly regularized formula (MPCJRF) is achieved. Third, by incorporating the MPCJRF into the framework of the conventional SVM, our approach, referred to as semi-supervised classification with extensive knowledge exploitation (SSC-EKE), is developed. The significance of our research is fourfold: 1) The MPCJRF is an underlying adjustment, with respect to the pairwise constraints, to the graph Laplacian enlisted for approximating the potential data manifold. This type of adjustment plays the correction role, as an unbiased estimation of the data manifold is difficult to obtain, whereas the pairwise constraints, converted from the given labels, have an overall high confidence level. 2) By transforming the values of the two terms in the MPCJRF such that they have the same range, with a trade-off factor varying within the invariant interval [0, 1), the appropriate impact of the pairwise constraints to the graph Laplacian can be self-adaptively determined. 3) The implication regarding extensive knowledge exploitation is embodied in SSC-EKE. That is, the labelled examples are used not only to control the empirical risk but also to constitute the MPCJRF. Moreover, all data, both labelled and unlabelled, are recruited for the model smoothness and manifold regularization. 4) The complete framework of SSC-EKE organically incorporates multiple

  16. A hierarchical classification scheme of psoriasis images

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

    2003-01-01

    A two-stage hierarchical classification scheme of psoriasis lesion images is proposed. These images are basically composed of three classes: normal skin, lesion and background. The scheme combines conventional tools to separate the skin from the background in the first stage, and the lesion from...

  17. Supervised learning classification models for prediction of plant virus encoded RNA silencing suppressors.

    Directory of Open Access Journals (Sweden)

    Zeenia Jagga

    Full Text Available Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a

  18. A hierarchical approach of hybrid image classification for land use and land cover mapping

    Directory of Open Access Journals (Sweden)

    Rahdari Vahid

    2018-01-01

    Full Text Available Remote sensing data analysis can provide thematic maps describing land-use and land-cover (LULC in a short period. Using proper image classification method in an area, is important to overcome the possible limitations of satellite imageries for producing land-use and land-cover maps. In the present study, a hierarchical hybrid image classification method was used to produce LULC maps using Landsat Thematic mapper TM for the year of 1998 and operational land imager OLI for the year of 2016. Images were classified using the proposed hybrid image classification method, vegetation cover crown percentage map from normalized difference vegetation index, Fisher supervised classification and object-based image classification methods. Accuracy assessment results showed that the hybrid classification method produced maps with total accuracy up to 84 percent with kappa statistic value 0.81. Results of this study showed that the proposed classification method worked better with OLI sensor than with TM. Although OLI has a higher radiometric resolution than TM, the produced LULC map using TM is almost accurate like OLI, which is because of LULC definitions and image classification methods used.

  19. Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps

    Science.gov (United States)

    Süveges, M.; Barblan, F.; Lecoeur-Taïbi, I.; Prša, A.; Holl, B.; Eyer, L.; Kochoska, A.; Mowlavi, N.; Rimoldini, L.

    2017-07-01

    Context. Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. Aims: We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We intend to estimate class labels according to two different, well-known classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, and consider their use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. Methods: We have explored the performance of principal component analysis (PCA), linear discriminant analysis (LDA), Random Forest classification and self-organizing maps (SOM) for the above aims. We pre-processed the photometric time series by combining a double Gaussian profile fit and a constrained smoothing spline, in order to de-noise and interpolate the observed light curves. We achieved further denoising, and selected the most important variability elements from the light curves using PCA. Supervised classification was performed using Random Forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimated the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed

  20. Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    YANG Zhaoxia

    2015-07-01

    Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

  1. Supervised remote sensing image classification: An example of a ...

    African Journals Online (AJOL)

    These conventional multi-class classifiers/algorithms are usually written in programming languages such as C, C++, and python. The objective of this research is to experiment the use of a binary classifier/algorithm for multi-class remote sensing task, implemented in MATLAB. MATLAB is a programming language just like C ...

  2. Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours.

    Directory of Open Access Journals (Sweden)

    Monique A Ladds

    Full Text Available Constructing activity budgets for marine animals when they are at sea and cannot be directly observed is challenging, but recent advances in bio-logging technology offer solutions to this problem. Accelerometers can potentially identify a wide range of behaviours for animals based on unique patterns of acceleration. However, when analysing data derived from accelerometers, there are many statistical techniques available which when applied to different data sets produce different classification accuracies. We investigated a selection of supervised machine learning methods for interpreting behavioural data from captive otariids (fur seals and sea lions. We conducted controlled experiments with 12 seals, where their behaviours were filmed while they were wearing 3-axis accelerometers. From video we identified 26 behaviours that could be grouped into one of four categories (foraging, resting, travelling and grooming representing key behaviour states for wild seals. We used data from 10 seals to train four predictive classification models: stochastic gradient boosting (GBM, random forests, support vector machine using four different kernels and a baseline model: penalised logistic regression. We then took the best parameters from each model and cross-validated the results on the two seals unseen so far. We also investigated the influence of feature statistics (describing some characteristic of the seal, testing the models both with and without these. Cross-validation accuracies were lower than training accuracy, but the SVM with a polynomial kernel was still able to classify seal behaviour with high accuracy (>70%. Adding feature statistics improved accuracies across all models tested. Most categories of behaviour -resting, grooming and feeding-were all predicted with reasonable accuracy (52-81% by the SVM while travelling was poorly categorised (31-41%. These results show that model selection is important when classifying behaviour and that by using

  3. Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery

    Directory of Open Access Journals (Sweden)

    Ramakalavathi Marapareddy

    2017-10-01

    Full Text Available This paper analyzes the use of a synthetic aperture radar (SAR imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides. If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD using Euclidean and Mahalanobis distance, support vector machine (SVM, and maximum likelihood (ML, using SAR technology to detect slough slides on earthen levees. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s uninhabited aerial vehicle synthetic aperture radar (UAVSAR. The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers.

  4. Multi-Label Classification by Semi-Supervised Singular Value Decomposition.

    Science.gov (United States)

    Jing, Liping; Shen, Chenyang; Yang, Liu; Yu, Jian; Ng, Michael K

    2017-10-01

    Multi-label problems arise in various domains, including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labelled data or even missing labelled data. In this paper, we proposed to use a semi-supervised singular value decomposition (SVD) to handle these two challenges. The proposed model takes advantage of the nuclear norm regularization on the SVD to effectively capture the label correlations. Meanwhile, it introduces manifold regularization on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labelled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve the proposed model based on the alternating direction method of multipliers, and thus, it can efficiently deal with large-scale data sets. Experimental results for synthetic and real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than the state-of-the-art methods.

  5. Large margin image set representation and classification

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-07-06

    In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation - maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency.

  6. Large margin image set representation and classification

    KAUST Repository

    Wang, Jim Jing-Yan; Alzahrani, Majed A.; Gao, Xin

    2014-01-01

    In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation - maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency.

  7. Fast Image Texture Classification Using Decision Trees

    Science.gov (United States)

    Thompson, David R.

    2011-01-01

    Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

  8. Map Classification In Image Data

    Science.gov (United States)

    2015-09-25

    showing the signicant portion of image and video data transfers via Youtube , Facebook, and Flickr as primary platforms from Infographic (2015) digital...reserves • hydrography: lakes, rivers, streams, swamps, coastal flats • relief: mountains, valleys, slopes, depressions • vegetation: wooded and cleared

  9. Bayesian Classification of Image Structures

    DEFF Research Database (Denmark)

    Goswami, Dibyendu; Kalkan, Sinan; Krüger, Norbert

    2009-01-01

    In this paper, we describe work on Bayesian classi ers for distinguishing between homogeneous structures, textures, edges and junctions. We build semi-local classiers from hand-labeled images to distinguish between these four different kinds of structures based on the concept of intrinsic dimensi...

  10. Attribute Learning for SAR Image Classification

    Directory of Open Access Journals (Sweden)

    Chu He

    2017-04-01

    Full Text Available This paper presents a classification approach based on attribute learning for high spatial resolution Synthetic Aperture Radar (SAR images. To explore the representative and discriminative attributes of SAR images, first, an iterative unsupervised algorithm is designed to cluster in the low-level feature space, where the maximum edge response and the ratio of mean-to-variance are included; a cross-validation step is applied to prevent overfitting. Second, the most discriminative clustering centers are sorted out to construct an attribute dictionary. By resorting to the attribute dictionary, a representation vector describing certain categories in the SAR image can be generated, which in turn is used to perform the classifying task. The experiments conducted on TerraSAR-X images indicate that those learned attributes have strong visual semantics, which are characterized by bright and dark spots, stripes, or their combinations. The classification method based on these learned attributes achieves better results.

  11. Prediction of lung cancer patient survival via supervised machine learning classification techniques.

    Science.gov (United States)

    Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

    2017-12-01

    Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time

  12. Segmentation and Classification of Burn Color Images

    Science.gov (United States)

    2001-10-25

    SEGMENTATION AND CLASSIFICATION OF BURN COLOR IMAGES Begoña Acha1, Carmen Serrano1, Laura Roa2 1Área de Teoría de la Señal y Comunicaciones ...2000, Las Vegas (USA), pp. 411-415. [21] G. Wyszecki and W.S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (New

  13. Segmentation and Classification of Burn Color Images

    National Research Council Canada - National Science Library

    Acha, Begonya

    2001-01-01

    .... In the classification part, we take advantage of color information by clustering, with a vector quantization algorithm, the color centroids of small squares, taken from the burnt segmented part of the image, in the (V1, V2) plane into two possible groups, where V1 and V2 are the two chrominance components of the CIE Lab representation.

  14. Improving settlement type classification of aerial images

    CSIR Research Space (South Africa)

    Mdakane, L

    2014-10-01

    Full Text Available , an automated method can be used to help identify human settlements in a fixed, repeatable and timely manner. The main contribution of this work is to improve generalisation on settlement type classification of aerial imagery. Images acquired at different dates...

  15. Multispectral and Panchromatic used Enhancement Resolution and Study Effective Enhancement on Supervised and Unsupervised Classification Land – Cover

    Science.gov (United States)

    Salman, S. S.; Abbas, W. A.

    2018-05-01

    The goal of the study is to support analysis Enhancement of Resolution and study effect on classification methods on bands spectral information of specific and quantitative approaches. In this study introduce a method to enhancement resolution Landsat 8 of combining the bands spectral of 30 meters resolution with panchromatic band 8 of 15 meters resolution, because of importance multispectral imagery to extracting land - cover. Classification methods used in this study to classify several lands -covers recorded from OLI- 8 imagery. Two methods of Data mining can be classified as either supervised or unsupervised. In supervised methods, there is a particular predefined target, that means the algorithm learn which values of the target are associated with which values of the predictor sample. K-nearest neighbors and maximum likelihood algorithms examine in this work as supervised methods. In other hand, no sample identified as target in unsupervised methods, the algorithm of data extraction searches for structure and patterns between all the variables, represented by Fuzzy C-mean clustering method as one of the unsupervised methods, NDVI vegetation index used to compare the results of classification method, the percent of dense vegetation in maximum likelihood method give a best results.

  16. Cluster Validity Classification Approaches Based on Geometric Probability and Application in the Classification of Remotely Sensed Images

    Directory of Open Access Journals (Sweden)

    LI Jian-Wei

    2014-08-01

    Full Text Available On the basis of the cluster validity function based on geometric probability in literature [1, 2], propose a cluster analysis method based on geometric probability to process large amount of data in rectangular area. The basic idea is top-down stepwise refinement, firstly categories then subcategories. On all clustering levels, use the cluster validity function based on geometric probability firstly, determine clusters and the gathering direction, then determine the center of clustering and the border of clusters. Through TM remote sensing image classification examples, compare with the supervision and unsupervised classification in ERDAS and the cluster analysis method based on geometric probability in two-dimensional square which is proposed in literature 2. Results show that the proposed method can significantly improve the classification accuracy.

  17. Supervised local error estimation for nonlinear image registration using convolutional neural networks

    NARCIS (Netherlands)

    Eppenhof, Koen A.J.; Pluim, Josien P.W.; Styner, M.A.; Angelini, E.D.

    2017-01-01

    Error estimation in medical image registration is valuable when validating, comparing, or combining registration methods. To validate a nonlinear image registration method, ideally the registration error should be known for the entire image domain. We propose a supervised method for the estimation

  18. Classification of Pansharpened Urban Satellite Images

    DEFF Research Database (Denmark)

    Palsson, Frosti; Sveinsson, Johannes R.; Benediktsson, Jon Atli

    2012-01-01

    The classification of high resolution urban remote sensing imagery is addressed with the focus on classification of imagery that has been pansharpened by a number of different pansharpening methods. The pansharpening process introduces some spectral and spatial distortions in the resulting fused...... multispectral image, the amount of which highly varies depending on which pansharpening technique is used. In the majority of the pansharpening techniques that have been proposed, there is a compromise between the spatial enhancement and the spectral consistency. Here we study the effects of the spectral...... information from the panchromatic data. Random Forests (RF) and Support Vector Machines (SVM) will be used as classifiers. Experiments are done for three different datasets that have been obtained by two different imaging sensors, IKONOS and QuickBird. These sensors deliver multispectral images that have four...

  19. Contribution of non-negative matrix factorization to the classification of remote sensing images

    Science.gov (United States)

    Karoui, M. S.; Deville, Y.; Hosseini, S.; Ouamri, A.; Ducrot, D.

    2008-10-01

    Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. The classification process requires some pre-processing, especially for data size reduction. The most usual technique is Principal Component Analysis. Another approach consists in regarding each pixel of the multispectral image as a mixture of pure elements contained in the observed area. Using Blind Source Separation (BSS) methods, one can hope to unmix each pixel and to perform the recognition of the classes constituting the observed scene. Our contribution consists in using Non-negative Matrix Factorization (NMF) combined with sparse coding as a solution to BSS, in order to generate new images (which are at least partly separated images) using HRV SPOT images from Oran area, Algeria). These images are then used as inputs of a supervised classifier integrating textural information. The results of classifications of these "separated" images show a clear improvement (correct pixel classification rate improved by more than 20%) compared to classification of initial (i.e. non separated) images. These results show the contribution of NMF as an attractive pre-processing for classification of multispectral remote sensing imagery.

  20. Classification and Diagnostic Output Prediction of Cancer Using Gene Expression Profiling and Supervised Machine Learning Algorithms

    DEFF Research Database (Denmark)

    Yoo, C.; Gernaey, Krist

    2008-01-01

    importance in the projection (VIP) information of the DPLS method. The power of the gene selection method and the proposed supervised hierarchical clustering method is illustrated on a three microarray data sets of leukemia, breast, and colon cancer. Supervised machine learning algorithms thus enable...

  1. Retrieval and classification of food images.

    Science.gov (United States)

    Farinella, Giovanni Maria; Allegra, Dario; Moltisanti, Marco; Stanco, Filippo; Battiato, Sebastiano

    2016-10-01

    Automatic food understanding from images is an interesting challenge with applications in different domains. In particular, food intake monitoring is becoming more and more important because of the key role that it plays in health and market economies. In this paper, we address the study of food image processing from the perspective of Computer Vision. As first contribution we present a survey of the studies in the context of food image processing from the early attempts to the current state-of-the-art methods. Since retrieval and classification engines able to work on food images are required to build automatic systems for diet monitoring (e.g., to be embedded in wearable cameras), we focus our attention on the aspect of the representation of the food images because it plays a fundamental role in the understanding engines. The food retrieval and classification is a challenging task since the food presents high variableness and an intrinsic deformability. To properly study the peculiarities of different image representations we propose the UNICT-FD1200 dataset. It was composed of 4754 food images of 1200 distinct dishes acquired during real meals. Each food plate is acquired multiple times and the overall dataset presents both geometric and photometric variabilities. The images of the dataset have been manually labeled considering 8 categories: Appetizer, Main Course, Second Course, Single Course, Side Dish, Dessert, Breakfast, Fruit. We have performed tests employing different representations of the state-of-the-art to assess the related performances on the UNICT-FD1200 dataset. Finally, we propose a new representation based on the perceptual concept of Anti-Textons which is able to encode spatial information between Textons outperforming other representations in the context of food retrieval and Classification. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the NSA

    Science.gov (United States)

    Hall, Forrest G. (Editor); Knapp, David

    2000-01-01

    The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood classification approach was used to produce this classification. The data are provided in a binary image format file. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

  3. Hyperspectral Image Classification Using Discriminative Dictionary Learning

    International Nuclear Information System (INIS)

    Zongze, Y; Hao, S; Kefeng, J; Huanxin, Z

    2014-01-01

    The hyperspectral image (HSI) processing community has witnessed a surge of papers focusing on the utilization of sparse prior for effective HSI classification. In sparse representation based HSI classification, there are two phases: sparse coding with an over-complete dictionary and classification. In this paper, we first apply a novel fisher discriminative dictionary learning method, which capture the relative difference in different classes. The competitive selection strategy ensures that atoms in the resulting over-complete dictionary are the most discriminative. Secondly, motivated by the assumption that spatially adjacent samples are statistically related and even belong to the same materials (same class), we propose a majority voting scheme incorporating contextual information to predict the category label. Experiment results show that the proposed method can effectively strengthen relative discrimination of the constructed dictionary, and incorporating with the majority voting scheme achieve generally an improved prediction performance

  4. SVM Pixel Classification on Colour Image Segmentation

    Science.gov (United States)

    Barui, Subhrajit; Latha, S.; Samiappan, Dhanalakshmi; Muthu, P.

    2018-04-01

    The aim of image segmentation is to simplify the representation of an image with the help of cluster pixels into something meaningful to analyze. Segmentation is typically used to locate boundaries and curves in an image, precisely to label every pixel in an image to give each pixel an independent identity. SVM pixel classification on colour image segmentation is the topic highlighted in this paper. It holds useful application in the field of concept based image retrieval, machine vision, medical imaging and object detection. The process is accomplished step by step. At first we need to recognize the type of colour and the texture used as an input to the SVM classifier. These inputs are extracted via local spatial similarity measure model and Steerable filter also known as Gabon Filter. It is then trained by using FCM (Fuzzy C-Means). Both the pixel level information of the image and the ability of the SVM Classifier undergoes some sophisticated algorithm to form the final image. The method has a well developed segmented image and efficiency with respect to increased quality and faster processing of the segmented image compared with the other segmentation methods proposed earlier. One of the latest application result is the Light L16 camera.

  5. Hybrid image classification technique for land-cover mapping in the Arctic tundra, North Slope, Alaska

    Science.gov (United States)

    Chaudhuri, Debasish

    Remotely sensed image classification techniques are very useful to understand vegetation patterns and species combination in the vast and mostly inaccessible arctic region. Previous researches that were done for mapping of land cover and vegetation in the remote areas of northern Alaska have considerably low accuracies compared to other biomes. The unique arctic tundra environment with short growing season length, cloud cover, low sun angles, snow and ice cover hinders the effectiveness of remote sensing studies. The majority of image classification research done in this area as reported in the literature used traditional unsupervised clustering technique with Landsat MSS data. It was also emphasized by previous researchers that SPOT/HRV-XS data lacked the spectral resolution to identify the small arctic tundra vegetation parcels. Thus, there is a motivation and research need to apply a new classification technique to develop an updated, detailed and accurate vegetation map at a higher spatial resolution i.e. SPOT-5 data. Traditional classification techniques in remotely sensed image interpretation are based on spectral reflectance values with an assumption of the training data being normally distributed. Hence it is difficult to add ancillary data in classification procedures to improve accuracy. The purpose of this dissertation was to develop a hybrid image classification approach that effectively integrates ancillary information into the classification process and combines ISODATA clustering, rule-based classifier and the Multilayer Perceptron (MLP) classifier which uses artificial neural network (ANN). The main goal was to find out the best possible combination or sequence of classifiers for typically classifying tundra type vegetation that yields higher accuracy than the existing classified vegetation map from SPOT data. Unsupervised ISODATA clustering and rule-based classification techniques were combined to produce an intermediate classified map which was

  6. SQL based cardiovascular ultrasound image classification.

    Science.gov (United States)

    Nandagopalan, S; Suryanarayana, Adiga B; Sudarshan, T S B; Chandrashekar, Dhanalakshmi; Manjunath, C N

    2013-01-01

    This paper proposes a novel method to analyze and classify the cardiovascular ultrasound echocardiographic images using Naïve-Bayesian model via database OLAP-SQL. Efficient data mining algorithms based on tightly-coupled model is used to extract features. Three algorithms are proposed for classification namely Naïve-Bayesian Classifier for Discrete variables (NBCD) with SQL, NBCD with OLAP-SQL, and Naïve-Bayesian Classifier for Continuous variables (NBCC) using OLAP-SQL. The proposed model is trained with 207 patient images containing normal and abnormal categories. Out of the three proposed algorithms, a high classification accuracy of 96.59% was achieved from NBCC which is better than the earlier methods.

  7. Classifications of objects on hyperspectral images

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey

    . In the present work a classification method that combines classic image classification approach and MIA is proposed. The basic idea is to group all pixels and calculate spectral properties of the pixel group to be used further as a vector of predictors for calibration and class prediction. The grouping can...... be done with mathematical morphology methods applied to a score image where objects are well separated. In the case of small overlapping a watershed transformation can be applied to disjoint the objects. The method has been tested on several simulated and real cases and showed good results and significant...... improvements in comparison with a standard MIA approach. The results as well as method details will be reported....

  8. Classification Of Cluster Area Forsatellite Image

    Directory of Open Access Journals (Sweden)

    Thwe Zin Phyo

    2015-06-01

    Full Text Available Abstract This paper describes area classification for Landsat7 satellite image. The main purpose of this system is to classify the area of each cluster contained in a satellite image. To classify this image firstly need to clusterthe satellite image into different land cover types. Clustering is an unsupervised learning method that aimsto classify an image into homogeneous regions. This system is implemented based on color features with K-means clustering unsupervised algorithm. This method does not need to train image before clustering.The clusters of satellite image are grouped into a set of three clusters for Landsat7 satellite image. For this work the combined band 432 from Landsat7 satellite is used as an input. Satellite imageMandalay area in 2001 is chosen to test the segmentation method. After clustering a specific range for three clustered images must be defined in order to obtain greenland water and urbanbalance.This system is implemented by using MATLAB programming language.

  9. Semantic Segmentation of Convolutional Neural Network for Supervised Classification of Multispectral Remote Sensing

    Science.gov (United States)

    Xue, L.; Liu, C.; Wu, Y.; Li, H.

    2018-04-01

    Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remote Sensing Imagery is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for ground object segmentation and the results could be further improved. This paper used convolution neural network named U-Net, its structure has a contracting path and an expansive path to get high resolution output. In the network , We added BN layers, which is more conducive to the reverse pass. Moreover, after upsampling convolution , we add dropout layers to prevent overfitting. They are promoted to get more precise segmentation results. To verify this network architecture, we used a Kaggle dataset. Experimental results show that U-Net achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

  10. Supervised Quality Assessment Of Medical Image Registration: Application to intra-patient CT lung registration

    NARCIS (Netherlands)

    Muenzing, S.E.; Ginneken, B. van; Murphy, K.; Pluim, J.P.

    2012-01-01

    A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal

  11. Supervised quality assessment of medical image registration : application to intra-patient CT lung registration

    NARCIS (Netherlands)

    Muenzing, S.E.A.; Ginneken, van B.; Murphy, K.; Pluim, J.P.W.

    2012-01-01

    A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal

  12. Efficient HIK SVM learning for image classification.

    Science.gov (United States)

    Wu, Jianxin

    2012-10-01

    Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms. This paper presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.

  13. An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

    Science.gov (United States)

    Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

    2015-01-01

    Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

  14. Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification.

    Science.gov (United States)

    Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried; De Vos, Winnok H

    2017-01-01

    A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows.

  15. Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification.

    Directory of Open Access Journals (Sweden)

    Marlies Verschuuren

    Full Text Available A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND, which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows.

  16. Supervising PTCA treatment with a scheme of steps combination SPECT 99Tcm-MIBI myocardial imaging

    International Nuclear Information System (INIS)

    Luan Zhaosheng; Tian Jiahe; Peng Yong; Zhou Wen; Gai Luyue; Sun Zhijun; Su Yuwen; Liu Xiaohu

    2001-01-01

    Objective: To set up a useful supervising method around PTCA treatment. Methods: A scheme of steps combination SPECT 99 Tc m -MIBI myocardial imaging was devised. 87 patients with coronary artery disease were selected into the study. 3-step imaging, exercise, rest and intravenous infusion of nitroglycerine (NTG) imaging, was performed at 1 week before PTCA. 2-step imaging, exercise and rest imaging, was performed 1-2 weeks after PTCA. All of the indexes obtained from steps combination imaging before and after PTCA were contrasted with each other in pairs. Results: 1) Compared with outcome of clinical assessing, the imaging after PTCA could correctly assess PTCA outcome. 2) The myocardial defects showed in 3-step imaging before PTCA appeared to be ameliorated after PTCA and the amelioration showed in exercise imaging was more evident than that in rest imaging. The myocardial perfusion state revealed by NTG imaging was similar to that revealed by rest imaging after PTCA. 3) The 3-step imaging findings correlated with PTCA outcome, the best correlation was found between indexes of myocardial defect changes showed by NTG and exercise imaging and indexes of PTCA outcome (r = 0.9470, P 99 Tc m -MIBI myocardial SPECT imaging scheme is a very useful supervising method around PTCA. 2-step imaging after PTCA could assess outcome correctly, and 3-step imaging before it could predict outcome correctly, too

  17. Wavelet-based multicomponent denoising on GPU to improve the classification of hyperspectral images

    Science.gov (United States)

    Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco; Mouriño, J. C.

    2017-10-01

    Supervised classification allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be beneficial for the interpretation of the image content, thus increasing the classification accuracy. Denoising in the spatial domain of the image has been shown as a technique that enhances the structures in the image. This paper proposes a multi-component denoising approach in order to increase the classification accuracy when a classification method is applied. It is computed on multicore CPUs and NVIDIA GPUs. The method combines feature extraction based on a 1Ddiscrete wavelet transform (DWT) applied in the spectral dimension followed by an Extended Morphological Profile (EMP) and a classifier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classification. The denoising recursively applies a separable 2D DWT after which the number of wavelet coefficients is reduced by using a threshold. Finally, inverse 2D-DWT filters are applied to reconstruct the noise free original component. The computational cost of the classifiers as well as the cost of the whole classification chain is high but it is reduced achieving real-time behavior for some applications through their computation on NVIDIA multi-GPU platforms.

  18. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

    Directory of Open Access Journals (Sweden)

    Sun Xun

    2016-12-01

    Full Text Available In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR images using multiple-feature fusion and ensemble learning. First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images. Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results. Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results. Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.

  19. Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

    Science.gov (United States)

    Guo, Yiqing; Jia, Xiuping; Paull, David

    2018-06-01

    The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

  20. Hyperspectral image classification using Support Vector Machine

    International Nuclear Information System (INIS)

    Moughal, T A

    2013-01-01

    Classification of land cover hyperspectral images is a very challenging task due to the unfavourable ratio between the number of spectral bands and the number of training samples. The focus in many applications is to investigate an effective classifier in terms of accuracy. The conventional multiclass classifiers have the ability to map the class of interest but the considerable efforts and large training sets are required to fully describe the classes spectrally. Support Vector Machine (SVM) is suggested in this paper to deal with the multiclass problem of hyperspectral imagery. The attraction to this method is that it locates the optimal hyper plane between the class of interest and the rest of the classes to separate them in a new high-dimensional feature space by taking into account only the training samples that lie on the edge of the class distributions known as support vectors and the use of the kernel functions made the classifier more flexible by making it robust against the outliers. A comparative study has undertaken to find an effective classifier by comparing Support Vector Machine (SVM) to the other two well known classifiers i.e. Maximum likelihood (ML) and Spectral Angle Mapper (SAM). At first, the Minimum Noise Fraction (MNF) was applied to extract the best possible features form the hyperspectral imagery and then the resulting subset of the features was applied to the classifiers. Experimental results illustrate that the integration of MNF and SVM technique significantly reduced the classification complexity and improves the classification accuracy.

  1. Dissimilarity Application in Digitized Mammographic Images Classification

    Directory of Open Access Journals (Sweden)

    Ubaldo Bottigli

    2006-06-01

    Full Text Available Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the traditional way of learning from examples of objects the classifiers are built in a feature space. However, an alternative ways can be found by constructing decision rules on dissimilarity (distance representations. In such a recognition process a new object is described by its distances to (a subset of the training samples. The use of the dissimilarities is especially of interest when features are difficult to obtain or when they have a little discriminative power. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs. Each ROI is characterized by some features extracted from co-occurrence matrix containing spatial statistics information on ROI pixel grey tones. A dissimilarity representation of these features is made before the classification. A feed-forward neural network is employed to distinguish pathological records, from non-pathological ones by the new features. The results obtained in terms of sensitivity and specificity will be presented.

  2. Novelty detection for breast cancer image classification

    Science.gov (United States)

    Cichosz, Pawel; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold

    2016-09-01

    Using classification learning algorithms for medical applications may require not only refined model creation techniques and careful unbiased model evaluation, but also detecting the risk of misclassification at the time of model application. This is addressed by novelty detection, which identifies instances for which the training set is not sufficiently representative and for which it may be safer to restrain from classification and request a human expert diagnosis. The paper investigates two techniques for isolated instance identification, based on clustering and one-class support vector machines, which represent two different approaches to multidimensional outlier detection. The prediction quality for isolated instances in breast cancer image data is evaluated using the random forest algorithm and found to be substantially inferior to the prediction quality for non-isolated instances. Each of the two techniques is then used to create a novelty detection model which can be combined with a classification model and used at the time of prediction to detect instances for which the latter cannot be reliably applied. Novelty detection is demonstrated to improve random forest prediction quality and argued to deserve further investigation in medical applications.

  3. Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

    Science.gov (United States)

    Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

    2009-01-01

    The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

  4. New Informative Features for Fault Diagnosis of Industrial Systems by Supervised Classification

    OpenAIRE

    Verron , Sylvain; Tiplica , Teodor; Kobi , Abdessamad

    2009-01-01

    International audience; The purpose of this article is to present a method for industrial process diagnosis. We are interested in fault diagnosis considered as a supervised classication task. The interest of the proposed method is to take into account new features (and so new informations) in the classifier. These new features are probabilities extracted from a Bayesian network comparing the faulty observations to the normal operating conditions. The performances of this method are evaluated ...

  5. Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer

    OpenAIRE

    Li, Chongyi; Guo, Jichang; Guo, Chunle

    2017-01-01

    Underwater vision suffers from severe effects due to selective attenuation and scattering when light propagates through water. Such degradation not only affects the quality of underwater images but limits the ability of vision tasks. Different from existing methods which either ignore the wavelength dependency of the attenuation or assume a specific spectral profile, we tackle color distortion problem of underwater image from a new view. In this letter, we propose a weakly supervised color tr...

  6. Semi-supervised rail defect detection from imbalanced image data

    NARCIS (Netherlands)

    Hajizadeh, S.; Nunez Vicencio, Alfredo; Tax, D.M.J.; Acarman, Tankut

    2016-01-01

    Rail defect detection by video cameras has recently gained much attention in both
    academia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semisupervised learning

  7. Iris Image Classification Based on Hierarchical Visual Codebook.

    Science.gov (United States)

    Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

    2014-06-01

    Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

  8. Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification.

    Science.gov (United States)

    Doostparast Torshizi, Abolfazl; Petzold, Linda R

    2018-01-01

    Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learning capabilities and lead to a better understanding of the underlying interactions among molecular levels. In this paper we present a graph-based semi-supervised classification algorithm that incorporates latent biological knowledge in the form of biological pathways with gene expression and DNA methylation data. The process of graph construction from biological pathways is based on detecting condition-responsive genes, where 3 sets of genes are finally extracted: all condition responsive genes, high-frequency condition-responsive genes, and P-value-filtered genes. The proposed approach is applied to ovarian cancer data downloaded from the Human Genome Atlas. Extensive numerical experiments demonstrate superior performance of the proposed approach compared to other state-of-the-art algorithms, including the latest graph-based classification techniques. Simulation results demonstrate that integrating various data types enhances classification performance and leads to a better understanding of interrelations between diverse omics data types. The proposed approach outperforms many of the state-of-the-art data integration algorithms. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  9. Band Subset Selection for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Chunyan Yu

    2018-01-01

    Full Text Available This paper develops a new approach to band subset selection (BSS for hyperspectral image classification (HSIC which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS, rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS, as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ and successive (SC algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.

  10. Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification.

    Science.gov (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng

    2017-05-03

    Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

  11. Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network

    Science.gov (United States)

    Cao, Xiangyong; Zhou, Feng; Xu, Lin; Meng, Deyu; Xu, Zongben; Paisley, John

    2018-05-01

    This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.

  12. Acne image analysis: lesion localization and classification

    Science.gov (United States)

    Abas, Fazly Salleh; Kaffenberger, Benjamin; Bikowski, Joseph; Gurcan, Metin N.

    2016-03-01

    Acne is a common skin condition present predominantly in the adolescent population, but may continue into adulthood. Scarring occurs commonly as a sequel to severe inflammatory acne. The presence of acne and resultant scars are more than cosmetic, with a significant potential to alter quality of life and even job prospects. The psychosocial effects of acne and scars can be disturbing and may be a risk factor for serious psychological concerns. Treatment efficacy is generally determined based on an invalidated gestalt by the physician and patient. However, the validated assessment of acne can be challenging and time consuming. Acne can be classified into several morphologies including closed comedones (whiteheads), open comedones (blackheads), papules, pustules, cysts (nodules) and scars. For a validated assessment, the different morphologies need to be counted independently, a method that is far too time consuming considering the limited time available for a consultation. However, it is practical to record and analyze images since dermatologists can validate the severity of acne within seconds after uploading an image. This paper covers the processes of region-ofinterest determination using entropy-based filtering and thresholding as well acne lesion feature extraction. Feature extraction methods using discrete wavelet frames and gray-level co-occurence matrix were presented and their effectiveness in separating the six major acne lesion classes were discussed. Several classifiers were used to test the extracted features. Correct classification accuracy as high as 85.5% was achieved using the binary classification tree with fourteen principle components used as descriptors. Further studies are underway to further improve the algorithm performance and validate it on a larger database.

  13. BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation

    Science.gov (United States)

    Vogel, Thomas; Nguyen, Dinh Quyen; Dittmann, Jana

    2006-01-01

    Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.

  14. Multi-level discriminative dictionary learning with application to large scale image classification.

    Science.gov (United States)

    Shen, Li; Sun, Gang; Huang, Qingming; Wang, Shuhui; Lin, Zhouchen; Wu, Enhua

    2015-10-01

    The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.

  15. Semantic Document Image Classification Based on Valuable Text Pattern

    Directory of Open Access Journals (Sweden)

    Hossein Pourghassem

    2011-01-01

    Full Text Available Knowledge extraction from detected document image is a complex problem in the field of information technology. This problem becomes more intricate when we know, a negligible percentage of the detected document images are valuable. In this paper, a segmentation-based classification algorithm is used to analysis the document image. In this algorithm, using a two-stage segmentation approach, regions of the image are detected, and then classified to document and non-document (pure region regions in the hierarchical classification. In this paper, a novel valuable definition is proposed to classify document image in to valuable or invaluable categories. The proposed algorithm is evaluated on a database consisting of the document and non-document image that provide from Internet. Experimental results show the efficiency of the proposed algorithm in the semantic document image classification. The proposed algorithm provides accuracy rate of 98.8% for valuable and invaluable document image classification problem.

  16. EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

    Science.gov (United States)

    Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

    2017-08-01

    Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

  17. Semi-Supervised Bayesian Classification of Materials with Impact-Echo Signals

    Directory of Open Access Journals (Sweden)

    Jorge Igual

    2015-05-01

    Full Text Available The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their defective status (homogeneous, one defect or multiple defects and kind of defect (hole or crack, passing through or not. Every specimen is impacted by a hammer, and the spectrum of the propagated wave is recorded. This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians. The parameters of the Gaussian mixtures and the class probabilities are estimated using an extended expectation-maximization algorithm. The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio. We test the method with real specimens made of aluminum alloy. The results show that the algorithm works very well. This technique could be applied in many industrial problems, such as the optimization of the marble cutting process.

  18. Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

    Energy Technology Data Exchange (ETDEWEB)

    Möller, A. [Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611 (Australia); Ruhlmann-Kleider, V.; Leloup, C.; Neveu, J.; Palanque-Delabrouille, N.; Rich, J. [Irfu, SPP, CEA Saclay, F-91191 Gif sur Yvette Cedex (France); Carlberg, R. [Department of Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H8 (Canada); Lidman, C. [Australian Astronomical Observatory, North Ryde, NSW 2113 (Australia); Pritchet, C., E-mail: anais.moller@anu.edu.au, E-mail: vanina.ruhlmann-kleider@cea.fr, E-mail: clement.leloup@cea.fr, E-mail: jneveu@lal.in2p3.fr, E-mail: nathalie.palanque-delabrouille@cea.fr, E-mail: james.rich@cea.fr, E-mail: raymond.carlberg@utoronto.ca, E-mail: chris.lidman@aao.gov.au, E-mail: pritchet@uvic.ca [Department of Physics and Astronomy, University of Victoria, P.O. Box 3055, Victoria, BC V8W 3P6 (Canada)

    2016-12-01

    In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts (0.2 < z < 1.1). Our method consists of two stages: feature extraction (obtaining the SN redshift from photometry and estimating light-curve shape parameters) and machine learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high- z SN survey with application to real SN data.

  19. Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

    International Nuclear Information System (INIS)

    Möller, A.; Ruhlmann-Kleider, V.; Leloup, C.; Neveu, J.; Palanque-Delabrouille, N.; Rich, J.; Carlberg, R.; Lidman, C.; Pritchet, C.

    2016-01-01

    In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts (0.2 < z < 1.1). Our method consists of two stages: feature extraction (obtaining the SN redshift from photometry and estimating light-curve shape parameters) and machine learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high- z SN survey with application to real SN data.

  20. Closing the loop: from paper to protein annotation using supervised Gene Ontology classification.

    Science.gov (United States)

    Gobeill, Julien; Pasche, Emilie; Vishnyakova, Dina; Ruch, Patrick

    2014-01-01

    Gene function curation of the literature with Gene Ontology (GO) concepts is one particularly time-consuming task in genomics, and the help from bioinformatics is highly requested to keep up with the flow of publications. In 2004, the first BioCreative challenge already designed a task of automatic GO concepts assignment from a full text. At this time, results were judged far from reaching the performances required by real curation workflows. In particular, supervised approaches produced the most disappointing results because of lack of training data. Ten years later, the available curation data have massively grown. In 2013, the BioCreative IV GO task revisited the automatic GO assignment task. For this issue, we investigated the power of our supervised classifier, GOCat. GOCat computes similarities between an input text and already curated instances contained in a knowledge base to infer GO concepts. The subtask A consisted in selecting GO evidence sentences for a relevant gene in a full text. For this, we designed a state-of-the-art supervised statistical approach, using a naïve Bayes classifier and the official training set, and obtained fair results. The subtask B consisted in predicting GO concepts from the previous output. For this, we applied GOCat and reached leading results, up to 65% for hierarchical recall in the top 20 outputted concepts. Contrary to previous competitions, machine learning has this time outperformed standard dictionary-based approaches. Thanks to BioCreative IV, we were able to design a complete workflow for curation: given a gene name and a full text, this system is able to select evidence sentences for curation and to deliver highly relevant GO concepts. Contrary to previous competitions, machine learning this time outperformed dictionary-based systems. Observed performances are sufficient for being used in a real semiautomatic curation workflow. GOCat is available at http://eagl.unige.ch/GOCat/. http://eagl.unige.ch/GOCat4FT/.

  1. Electronic structure classifications using scanning tunneling microscopy conductance imaging

    International Nuclear Information System (INIS)

    Horn, K.M.; Swartzentruber, B.S.; Osbourn, G.C.; Bouchard, A.; Bartholomew, J.W.

    1998-01-01

    The electronic structure of atomic surfaces is imaged by applying multivariate image classification techniques to multibias conductance data measured using scanning tunneling microscopy. Image pixels are grouped into classes according to shared conductance characteristics. The image pixels, when color coded by class, produce an image that chemically distinguishes surface electronic features over the entire area of a multibias conductance image. Such open-quotes classedclose quotes images reveal surface features not always evident in a topograph. This article describes the experimental technique used to record multibias conductance images, how image pixels are grouped in a mathematical, classification space, how a computed grouping algorithm can be employed to group pixels with similar conductance characteristics in any number of dimensions, and finally how the quality of the resulting classed images can be evaluated using a computed, combinatorial analysis of the full dimensional space in which the classification is performed. copyright 1998 American Institute of Physics

  2. Pixel Classification of SAR ice images using ANFIS-PSO Classifier

    Directory of Open Access Journals (Sweden)

    G. Vasumathi

    2016-12-01

    Full Text Available Synthetic Aperture Radar (SAR is playing a vital role in taking extremely high resolution radar images. It is greatly used to monitor the ice covered ocean regions. Sea monitoring is important for various purposes which includes global climate systems and ship navigation. Classification on the ice infested area gives important features which will be further useful for various monitoring process around the ice regions. Main objective of this paper is to classify the SAR ice image that helps in identifying the regions around the ice infested areas. In this paper three stages are considered in classification of SAR ice images. It starts with preprocessing in which the speckled SAR ice images are denoised using various speckle removal filters; comparison is made on all these filters to find the best filter in speckle removal. Second stage includes segmentation in which different regions are segmented using K-means and watershed segmentation algorithms; comparison is made between these two algorithms to find the best in segmenting SAR ice images. The last stage includes pixel based classification which identifies and classifies the segmented regions using various supervised learning classifiers. The algorithms includes Back propagation neural networks (BPN, Fuzzy Classifier, Adaptive Neuro Fuzzy Inference Classifier (ANFIS classifier and proposed ANFIS with Particle Swarm Optimization (PSO classifier; comparison is made on all these classifiers to propose which classifier is best suitable for classifying the SAR ice image. Various evaluation metrics are performed separately at all these three stages.

  3. Managing complex processing of medical image sequences by program supervision techniques

    Science.gov (United States)

    Crubezy, Monica; Aubry, Florent; Moisan, Sabine; Chameroy, Virginie; Thonnat, Monique; Di Paola, Robert

    1997-05-01

    Our objective is to offer clinicians wider access to evolving medical image processing (MIP) techniques, crucial to improve assessment and quantification of physiological processes, but difficult to handle for non-specialists in MIP. Based on artificial intelligence techniques, our approach consists in the development of a knowledge-based program supervision system, automating the management of MIP libraries. It comprises a library of programs, a knowledge base capturing the expertise about programs and data and a supervision engine. It selects, organizes and executes the appropriate MIP programs given a goal to achieve and a data set, with dynamic feedback based on the results obtained. It also advises users in the development of new procedures chaining MIP programs.. We have experimented the approach for an application of factor analysis of medical image sequences as a means of predicting the response of osteosarcoma to chemotherapy, with both MRI and NM dynamic image sequences. As a result our program supervision system frees clinical end-users from performing tasks outside their competence, permitting them to concentrate on clinical issues. Therefore our approach enables a better exploitation of possibilities offered by MIP and higher quality results, both in terms of robustness and reliability.

  4. Restructuring supervision and reconfiguration of skill mix in community pharmacy: Classification of perceived safety and risk.

    Science.gov (United States)

    Bradley, Fay; Willis, Sarah C; Noyce, Peter R; Schafheutle, Ellen I

    2016-01-01

    Broadening the range of services provided through community pharmacy increases workloads for pharmacists that could be alleviated by reconfiguring roles within the pharmacy team. To examine pharmacists' and pharmacy technicians (PTs)' perceptions of how safe it would be for support staff to undertake a range of pharmacy activities during a pharmacist's absence. Views on supervision, support staff roles, competency and responsibility were also sought. Informed by nominal group discussions, a questionnaire was developed and distributed to a random sample of 1500 pharmacists and 1500 PTs registered in England. Whilst focused on community pharmacy practice, hospital pharmacy respondents were included, as more advanced skill mix models may provide valuable insights. Respondents were asked to rank a list of 22 pharmacy activities in terms of perceived risk and safety of these activities being performed by support staff during a pharmacist's absence. Descriptive and comparative statistic analyses were conducted. Six-hundred-and-forty-two pharmacists (43.2%) and 854 PTs (57.3%) responded; the majority worked in community pharmacy. Dependent on agreement levels with perceived safety, from community pharmacists and PTs, and hospital pharmacists and PTs, the 22 activities were grouped into 'safe' (n = 7), 'borderline' (n = 9) and 'unsafe' (n = 6). Activities such as assembly and labeling were considered 'safe,' clinical activities were considered 'unsafe.' There were clear differences between pharmacists and PTs, and sectors (community pharmacy vs. hospital). Community pharmacists were most cautious (particularly mobile and portfolio pharmacists) about which activities they felt support staff could safely perform; PTs in both sectors felt significantly more confident performing particularly technical activities than pharmacists. This paper presents novel empirical evidence informing the categorization of pharmacy activities into 'safe,' 'borderline' or 'unsafe

  5. Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting

    KAUST Repository

    Fernandes, José Antonio

    2013-02-01

    A multi-species approach to fisheries management requires taking into account the interactions between species in order to improve recruitment forecasting of the fish species. Recent advances in Bayesian networks direct the learning of models with several interrelated variables to be forecasted simultaneously. These models are known as multi-dimensional Bayesian network classifiers (MDBNs). Pre-processing steps are critical for the posterior learning of the model in these kinds of domains. Therefore, in the present study, a set of \\'state-of-the-art\\' uni-dimensional pre-processing methods, within the categories of missing data imputation, feature discretization and feature subset selection, are adapted to be used with MDBNs. A framework that includes the proposed multi-dimensional supervised pre-processing methods, coupled with a MDBN classifier, is tested with synthetic datasets and the real domain of fish recruitment forecasting. The correctly forecasting of three fish species (anchovy, sardine and hake) simultaneously is doubled (from 17.3% to 29.5%) using the multi-dimensional approach in comparison to mono-species models. The probability assessments also show high improvement reducing the average error (estimated by means of Brier score) from 0.35 to 0.27. Finally, these differences are superior to the forecasting of species by pairs. © 2012 Elsevier Ltd.

  6. APPLICATION OF SENSOR FUSION TO IMPROVE UAV IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    S. Jabari

    2017-08-01

    Full Text Available Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan camera along with either a colour camera or a four-band multi-spectral (MS camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC. We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

  7. Application of Sensor Fusion to Improve Uav Image Classification

    Science.gov (United States)

    Jabari, S.; Fathollahi, F.; Zhang, Y.

    2017-08-01

    Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan) camera along with either a colour camera or a four-band multi-spectral (MS) camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC). We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

  8. Visualization and classification in biomedical terahertz pulsed imaging

    International Nuclear Information System (INIS)

    Loeffler, Torsten; Siebert, Karsten; Czasch, Stephanie; Bauer, Tobias; Roskos, Hartmut G

    2002-01-01

    'Visualization' in imaging is the process of extracting useful information from raw data in such a way that meaningful physical contrasts are developed. 'Classification' is the subsequent process of defining parameter ranges which allow us to identify elements of images such as different tissues or different objects. In this paper, we explore techniques for visualization and classification in terahertz pulsed imaging (TPI) for biomedical applications. For archived (formalin-fixed, alcohol-dehydrated and paraffin-mounted) test samples, we investigate both time- and frequency-domain methods based on bright- and dark-field TPI. Successful tissue classification is demonstrated

  9. Unsupervised feature learning for autonomous rock image classification

    Science.gov (United States)

    Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

    2017-09-01

    Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

  10. A probablistic neural network classification system for signal and image processing

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, B. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  11. Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation

    Institute of Scientific and Technical Information of China (English)

    Tian Dongping

    2017-01-01

    In recent years, multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas, especially for automatic image annotation, whose purpose is to provide an efficient and effective searching environment for users to query their images more easily.In this paper, a semi-supervised learning based probabilistic latent semantic analysis ( PL-SA) model for automatic image annotation is presenred.Since it' s often hard to obtain or create la-beled images in large quantities while unlabeled ones are easier to collect, a transductive support vector machine ( TSVM) is exploited to enhance the quality of the training image data.Then, differ-ent image features with different magnitudes will result in different performance for automatic image annotation.To this end, a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible.Finally, a PLSA model with asymmetric mo-dalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores.Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PL-SA for the task of automatic image annotation.

  12. Fast and robust segmentation of white blood cell images by self-supervised learning.

    Science.gov (United States)

    Zheng, Xin; Wang, Yong; Wang, Guoyou; Liu, Jianguo

    2018-04-01

    A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets. Copyright © 2018 Elsevier Ltd. All rights reserved.

  13. Significance of perceptually relevant image decolorization for scene classification

    Science.gov (United States)

    Viswanathan, Sowmya; Divakaran, Govind; Soman, Kutti Padanyl

    2017-11-01

    Color images contain luminance and chrominance components representing the intensity and color information, respectively. The objective of this paper is to show the significance of incorporating chrominance information to the task of scene classification. An improved color-to-grayscale image conversion algorithm that effectively incorporates chrominance information is proposed using the color-to-gray structure similarity index and singular value decomposition to improve the perceptual quality of the converted grayscale images. The experimental results based on an image quality assessment for image decolorization and its success rate (using the Cadik and COLOR250 datasets) show that the proposed image decolorization technique performs better than eight existing benchmark algorithms for image decolorization. In the second part of the paper, the effectiveness of incorporating the chrominance component for scene classification tasks is demonstrated using a deep belief network-based image classification system developed using dense scale-invariant feature transforms. The amount of chrominance information incorporated into the proposed image decolorization technique is confirmed with the improvement to the overall scene classification accuracy. Moreover, the overall scene classification performance improved by combining the models obtained using the proposed method and conventional decolorization methods.

  14. Hyperspectral Image Classification Using Kernel Fukunaga-Koontz Transform

    Directory of Open Access Journals (Sweden)

    Semih Dinç

    2013-01-01

    images. In experiment section, the improved performance of HSI classification technique, K-FKT, has been tested comparing other methods such as the classical FKT and three types of support vector machines (SVMs.

  15. ASSESSMENT OF LANDSCAPE CHARACTERISTICS ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    Science.gov (United States)

    Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of misclassifying pixels during thematic image classification. However, there has been a lack of empirical evidence, to support these hypotheses. This...

  16. Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

    Science.gov (United States)

    Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

    2017-07-01

    Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

  17. Quantitative Outline-based Shape Analysis and Classification of Planetary Craterforms using Supervised Learning Models

    Science.gov (United States)

    Slezak, Thomas Joseph; Radebaugh, Jani; Christiansen, Eric

    2017-10-01

    The shapes of craterform morphology on planetary surfaces provides rich information about their origins and evolution. While morphologic information provides rich visual clues to geologic processes and properties, the ability to quantitatively communicate this information is less easily accomplished. This study examines the morphology of craterforms using the quantitative outline-based shape methods of geometric morphometrics, commonly used in biology and paleontology. We examine and compare landforms on planetary surfaces using shape, a property of morphology that is invariant to translation, rotation, and size. We quantify the shapes of paterae on Io, martian calderas, terrestrial basaltic shield calderas, terrestrial ash-flow calderas, and lunar impact craters using elliptic Fourier analysis (EFA) and the Zahn and Roskies (Z-R) shape function, or tangent angle approach to produce multivariate shape descriptors. These shape descriptors are subjected to multivariate statistical analysis including canonical variate analysis (CVA), a multiple-comparison variant of discriminant analysis, to investigate the link between craterform shape and classification. Paterae on Io are most similar in shape to terrestrial ash-flow calderas and the shapes of terrestrial basaltic shield volcanoes are most similar to martian calderas. The shapes of lunar impact craters, including simple, transitional, and complex morphology, are classified with a 100% rate of success in all models. Multiple CVA models effectively predict and classify different craterforms using shape-based identification and demonstrate significant potential for use in the analysis of planetary surfaces.

  18. Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

    Science.gov (United States)

    Fesharaki, Nooshin Jafari; Pourghassem, Hossein

    2013-07-01

    Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

  19. MULTI-TEMPORAL CLASSIFICATION AND CHANGE DETECTION USING UAV IMAGES

    Directory of Open Access Journals (Sweden)

    S. Makuti

    2018-05-01

    Full Text Available In this paper different methodologies for the classification and change detection of UAV image blocks are explored. UAV is not only the cheapest platform for image acquisition but it is also the easiest platform to operate in repeated data collections over a changing area like a building construction site. Two change detection techniques have been evaluated in this study: the pre-classification and the post-classification algorithms. These methods are based on three main steps: feature extraction, classification and change detection. A set of state of the art features have been used in the tests: colour features (HSV, textural features (GLCM and 3D geometric features. For classification purposes Conditional Random Field (CRF has been used: the unary potential was determined using the Random Forest algorithm while the pairwise potential was defined by the fully connected CRF. In the performed tests, different feature configurations and settings have been considered to assess the performance of these methods in such challenging task. Experimental results showed that the post-classification approach outperforms the pre-classification change detection method. This was analysed using the overall accuracy, where by post classification have an accuracy of up to 62.6 % and the pre classification change detection have an accuracy of 46.5 %. These results represent a first useful indication for future works and developments.

  20. Object-Oriented Semisupervised Classification of VHR Images by Combining MedLDA and a Bilateral Filter

    Directory of Open Access Journals (Sweden)

    Shi He

    2015-01-01

    Full Text Available A Bayesian hierarchical model is presented to classify very high resolution (VHR images in a semisupervised manner, in which both a maximum entropy discrimination latent Dirichlet allocation (MedLDA and a bilateral filter are combined into a novel application framework. The primary contribution of this paper is to nullify the disadvantages of traditional probabilistic topic models on pixel-level supervised information and to achieve the effective classification of VHR remote sensing images. This framework consists of the following two iterative steps. In the training stage, the model utilizes the central labeled pixel and its neighborhood, as a squared labeled image object, to train the classifiers. In the classification stage, each central unlabeled pixel with its neighborhood, as an unlabeled object, is classified as a user-provided geoobject class label with the maximum posterior probability. Gibbs sampling is adopted for model inference. The experimental results demonstrate that the proposed method outperforms two classical SVM-based supervised classification methods and probabilistic-topic-models-based classification methods.

  1. Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data

    Directory of Open Access Journals (Sweden)

    Sarah J. Graves

    2016-02-01

    Full Text Available Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350–2500 nm of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62% ± 2.3% and F-score of 59% ± 2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over- and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation

  2. Supervised guiding long-short term memory for image caption generation based on object classes

    Science.gov (United States)

    Wang, Jian; Cao, Zhiguo; Xiao, Yang; Qi, Xinyuan

    2018-03-01

    The present models of image caption generation have the problems of image visual semantic information attenuation and errors in guidance information. In order to solve these problems, we propose a supervised guiding Long Short Term Memory model based on object classes, named S-gLSTM for short. It uses the object detection results from R-FCN as supervisory information with high confidence, and updates the guidance word set by judging whether the last output matches the supervisory information. S-gLSTM learns how to extract the current interested information from the image visual se-mantic information based on guidance word set. The interested information is fed into the S-gLSTM at each iteration as guidance information, to guide the caption generation. To acquire the text-related visual semantic information, the S-gLSTM fine-tunes the weights of the network through the back-propagation of the guiding loss. Complementing guidance information at each iteration solves the problem of visual semantic information attenuation in the traditional LSTM model. Besides, the supervised guidance information in our model can reduce the impact of the mismatched words on the caption generation. We test our model on MSCOCO2014 dataset, and obtain better performance than the state-of-the- art models.

  3. Image analysis and mathematical modelling for the supervision of the dough fermentation process

    Science.gov (United States)

    Zettel, Viktoria; Paquet-Durand, Olivier; Hecker, Florian; Hitzmann, Bernd

    2016-10-01

    The fermentation (proof) process of dough is one of the quality-determining steps in the production of baking goods. Beside the fluffiness, whose fundaments are built during fermentation, the flavour of the final product is influenced very much during this production stage. However, until now no on-line measurement system is available, which can supervise this important process step. In this investigation the potential of an image analysis system is evaluated, that enables the determination of the volume of fermented dough pieces. The camera is moving around the fermenting pieces and collects images from the objects by means of different angles (360° range). Using image analysis algorithms the volume increase of individual dough pieces is determined. Based on a detailed mathematical description of the volume increase, which based on the Bernoulli equation, carbon dioxide production rate of yeast cells and the diffusion processes of carbon dioxide, the fermentation process is supervised. Important process parameters, like the carbon dioxide production rate of the yeast cells and the dough viscosity can be estimated just after 300 s of proofing. The mean percentage error for forecasting the further evolution of the relative volume of the dough pieces is just 2.3 %. Therefore, a forecast of the further evolution can be performed and used for fault detection.

  4. Polarimetric SAR image classification based on discriminative dictionary learning model

    Science.gov (United States)

    Sang, Cheng Wei; Sun, Hong

    2018-03-01

    Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

  5. Virtual Satellite Construction and Application for Image Classification

    International Nuclear Information System (INIS)

    Su, W G; Su, F Z; Zhou, C H

    2014-01-01

    Nowadays, most remote sensing image classification uses single satellite remote sensing data, so the number of bands and band spectral width is consistent. In addition, observed phenomenon such as land cover have the same spectral signature, which causes the classification accuracy to decrease as different data have unique characteristic. Therefore, this paper analyzes different optical remote sensing satellites, comparing the spectral differences and proposes the ideas and methods to build a virtual satellite. This article illustrates the research on the TM, HJ-1 and MODIS data. We obtained the virtual band X 0 through these satellites' bands combined it with the 4 bands of a TM image to build a virtual satellite with five bands. Based on this, we used these data for image classification. The experimental results showed that the virtual satellite classification results of building land and water information were superior to the HJ-1 and TM data respectively

  6. Classification and overview of research in real-time imaging

    Science.gov (United States)

    Sinha, Purnendu; Gorinsky, Sergey V.; Laplante, Phillip A.; Stoyenko, Alexander D.; Marlowe, Thomas J.

    1996-10-01

    Real-time imaging has application in areas such as multimedia, virtual reality, medical imaging, and remote sensing and control. Recently, the imaging community has witnessed a tremendous growth in research and new ideas in these areas. To lend structure to this growth, we outline a classification scheme and provide an overview of current research in real-time imaging. For convenience, we have categorized references by research area and application.

  7. Deep learning for tumor classification in imaging mass spectrometry.

    Science.gov (United States)

    Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

    2018-04-01

    Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

  8. Statistical methods for segmentation and classification of images

    DEFF Research Database (Denmark)

    Rosholm, Anders

    1997-01-01

    The central matter of the present thesis is Bayesian statistical inference applied to classification of images. An initial review of Markov Random Fields relates to the modeling aspect of the indicated main subject. In that connection, emphasis is put on the relatively unknown sub-class of Pickard...... with a Pickard Random Field modeling of a considered (categorical) image phenomemon. An extension of the fast PRF based classification technique is presented. The modification introduces auto-correlation into the model of an involved noise process, which previously has been assumed independent. The suitability...... of the extended model is documented by tests on controlled image data containing auto-correlated noise....

  9. Hyperspectral image classification based on local binary patterns and PCANet

    Science.gov (United States)

    Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

    2018-04-01

    Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

  10. Preliminary hard and soft bottom seafloor substrate map derived from an supervised classification of bathymetry derived from multispectral World View-2 satellite imagery of Ni'ihau Island, Territory of Main Hawaiian Islands, USA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary hard and soft seafloor substrate map derived from a supervised classification from multispectral World View-2 satellite imagery of Ni'ihau Island,...

  11. Shadow detection and removal in RGB VHR images for land use unsupervised classification

    Science.gov (United States)

    Movia, A.; Beinat, A.; Crosilla, F.

    2016-09-01

    Nowadays, high resolution aerial images are widely available thanks to the diffusion of advanced technologies such as UAVs (Unmanned Aerial Vehicles) and new satellite missions. Although these developments offer new opportunities for accurate land use analysis and change detection, cloud and terrain shadows actually limit benefits and possibilities of modern sensors. Focusing on the problem of shadow detection and removal in VHR color images, the paper proposes new solutions and analyses how they can enhance common unsupervised classification procedures for identifying land use classes related to the CO2 absorption. To this aim, an improved fully automatic procedure has been developed for detecting image shadows using exclusively RGB color information, and avoiding user interaction. Results show a significant accuracy enhancement with respect to similar methods using RGB based indexes. Furthermore, novel solutions derived from Procrustes analysis have been applied to remove shadows and restore brightness in the images. In particular, two methods implementing the so called "anisotropic Procrustes" and the "not-centered oblique Procrustes" algorithms have been developed and compared with the linear correlation correction method based on the Cholesky decomposition. To assess how shadow removal can enhance unsupervised classifications, results obtained with classical methods such as k-means, maximum likelihood, and self-organizing maps, have been compared to each other and with a supervised clustering procedure.

  12. Classification of maize kernels using NIR hyperspectral imaging

    DEFF Research Database (Denmark)

    Williams, Paul; Kucheryavskiy, Sergey V.

    2016-01-01

    NIR hyperspectral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and soft. Two approaches, pixel-wise and object-wise, were investigated to group kernels according to hardness. The pixel-wise classification assigned a class to every pixel from individual...... and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale....

  13. Weakly Supervised Dictionary Learning

    Science.gov (United States)

    You, Zeyu; Raich, Raviv; Fern, Xiaoli Z.; Kim, Jinsub

    2018-05-01

    We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly learning a dictionary and corresponding sparse coefficients to provide accurate data representation. This approach is useful for denoising and signal restoration, but may lead to sub-optimal classification performance. By contrast, analysis dictionary learning provides a transform that maps data to a sparse discriminative representation suitable for classification. We consider the problem of analysis dictionary learning for time-series data under a weak supervision setting in which signals are assigned with a global label instead of an instantaneous label signal. We propose a discriminative probabilistic model that incorporates both label information and sparsity constraints on the underlying latent instantaneous label signal using cardinality control. We present the expectation maximization (EM) procedure for maximum likelihood estimation (MLE) of the proposed model. To facilitate a computationally efficient E-step, we propose both a chain and a novel tree graph reformulation of the graphical model. The performance of the proposed model is demonstrated on both synthetic and real-world data.

  14. An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

    Directory of Open Access Journals (Sweden)

    Muhammad Ali

    2017-11-01

    Full Text Available Current transformer (CT saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL methods have brought a subversive revolution in the field of artificial intelligence (AI. This paper presents a new DL classification method based on unsupervised feature extraction and supervised fine-tuning strategy to classify the saturated and unsaturated regions in case of CT saturation. In other words, if protection system is subjected to a CT saturation, proposed method will correctly classify the different levels of saturation with a high accuracy. Traditional AI methods are mostly based on supervised learning and rely heavily on human crafted features. This paper contributes to an unsupervised feature extraction, using autoencoders and deep neural networks (DNNs to extract features automatically without prior knowledge of optimal features. To validate the effectiveness of proposed method, a variety of simulation tests are conducted, and classification results are analyzed using standard classification metrics. Simulation results confirm that proposed method classifies the different levels of CT saturation with a remarkable accuracy and has unique feature extraction capabilities. Lastly, we provided a potential future research direction to conclude this paper.

  15. Supervised variational model with statistical inference and its application in medical image segmentation.

    Science.gov (United States)

    Li, Changyang; Wang, Xiuying; Eberl, Stefan; Fulham, Michael; Yin, Yong; Dagan Feng, David

    2015-01-01

    Automated and general medical image segmentation can be challenging because the foreground and the background may have complicated and overlapping density distributions in medical imaging. Conventional region-based level set algorithms often assume piecewise constant or piecewise smooth for segments, which are implausible for general medical image segmentation. Furthermore, low contrast and noise make identification of the boundaries between foreground and background difficult for edge-based level set algorithms. Thus, to address these problems, we suggest a supervised variational level set segmentation model to harness the statistical region energy functional with a weighted probability approximation. Our approach models the region density distributions by using the mixture-of-mixtures Gaussian model to better approximate real intensity distributions and distinguish statistical intensity differences between foreground and background. The region-based statistical model in our algorithm can intuitively provide better performance on noisy images. We constructed a weighted probability map on graphs to incorporate spatial indications from user input with a contextual constraint based on the minimization of contextual graphs energy functional. We measured the performance of our approach on ten noisy synthetic images and 58 medical datasets with heterogeneous intensities and ill-defined boundaries and compared our technique to the Chan-Vese region-based level set model, the geodesic active contour model with distance regularization, and the random walker model. Our method consistently achieved the highest Dice similarity coefficient when compared to the other methods.

  16. Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

    Science.gov (United States)

    Bach, H.; Klug, P.; Ruf, T.; Migdall, S.; Schlenz, F.; Hank, T.; Mauser, W.

    2015-04-01

    To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is

  17. Image classification independent of orientation and scale

    Science.gov (United States)

    Arsenault, Henri H.; Parent, Sebastien; Moisan, Sylvain

    1998-04-01

    The recognition of targets independently of orientation has become fairly well developed in recent years for in-plane rotation. The out-of-plane rotation problem is much less advanced. When both out-of-plane rotations and changes of scale are present, the problem becomes very difficult. In this paper we describe our research on the combined out-of- plane rotation problem and the scale invariance problem. The rotations were limited to rotations about an axis perpendicular to the line of sight. The objects to be classified were three kinds of military vehicles. The inputs used were infrared imagery and photographs. We used a variation of a method proposed by Neiberg and Casasent, where a neural network is trained with a subset of the database and a minimum distances from lines in feature space are used for classification instead of nearest neighbors. Each line in the feature space corresponds to one class of objects, and points on one line correspond to different orientations of the same target. We found that the training samples needed to be closer for some orientations than for others, and that the most difficult orientations are where the target is head-on to the observer. By means of some additional training of the neural network, we were able to achieve 100% correct classification for 360 degree rotation and a range of scales over a factor of five.

  18. Vegetation classification and quatification by satellite image processing. A case study in north Portugal

    Energy Technology Data Exchange (ETDEWEB)

    Aranha, J.T. [Dept. Florestal, UTAD, 5001-801 Vila Real (Portugal); Viana, H.F. [Instituto Politecnico de Viseu, Escola Superior Agraria, Viseu (Portugal); Rodrigues, R. [Bioflag - Consulting - Santo Tirso (Portugal)

    2008-07-01

    The expected increase in Forest Biomass demand for energy production leads to derive expeditious and non-expensive techniques in order to classify vegetal land cover and evaluate the available biomass like to be harvested. Satellite image processing and classification, combined to field work, is a suitable tool to achieve these aims. A vegetation index (NDVI) was created by means of a Landsat TM image, from 2006, manipulation, in order to create a general vegetation map. Then, the same image was submitted to a supervised classification process in order to produce a land cover map (overall accuracy of 85%). In a second stage, they were collected NDVI values for each sampling plot, in order to update the database previous developed with data collected within forestry stands and shrubland. This data merging enabled to transform general vegetation map into available biomass within forestry stands and shrubland. The results showed a range of values from 0.25 up to 6.00 dry ton./ha for recent and former burnt areas recovered by Pinus pinaster (maritime pine) young trees and from 2.00 up to 9.00 dry ton./ha for recent and former burnt areas recovered by shrubs (e.g. genista or broom).

  19. Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares

    Directory of Open Access Journals (Sweden)

    Jianjun Liu

    2017-11-01

    Full Text Available As a widely used classifier, sparse representation classification (SRC has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other norms (e.g., ℓ 2 -norm can be used to regularize the coding coefficients, except for the sparsity ℓ 1 -norm. In this paper, we show that in the kernel space the nonnegative constraint can also play the same role, and thus suggest the investigation of kernel fully constrained least squares (KFCLS for hyperspectral image classification. Furthermore, in order to improve the classification performance of KFCLS by incorporating spatial-spectral information, we investigate two kinds of spatial-spectral methods using two regularization strategies: (1 the coefficient-level regularization strategy, and (2 the class-level regularization strategy. Experimental results conducted on four real hyperspectral images demonstrate the effectiveness of the proposed KFCLS, and show which way to incorporate spatial-spectral information efficiently in the regularization framework.

  20. Time Series of Images to Improve Tree Species Classification

    Science.gov (United States)

    Miyoshi, G. T.; Imai, N. N.; de Moraes, M. V. A.; Tommaselli, A. M. G.; Näsi, R.

    2017-10-01

    Tree species classification provides valuable information to forest monitoring and management. The high floristic variation of the tree species appears as a challenging issue in the tree species classification because the vegetation characteristics changes according to the season. To help to monitor this complex environment, the imaging spectroscopy has been largely applied since the development of miniaturized sensors attached to Unmanned Aerial Vehicles (UAV). Considering the seasonal changes in forests and the higher spectral and spatial resolution acquired with sensors attached to UAV, we present the use of time series of images to classify four tree species. The study area is an Atlantic Forest area located in the western part of São Paulo State. Images were acquired in August 2015 and August 2016, generating three data sets of images: only with the image spectra of 2015; only with the image spectra of 2016; with the layer stacking of images from 2015 and 2016. Four tree species were classified using Spectral angle mapper (SAM), Spectral information divergence (SID) and Random Forest (RF). The results showed that SAM and SID caused an overfitting of the data whereas RF showed better results and the use of the layer stacking improved the classification achieving a kappa coefficient of 18.26 %.

  1. Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

    Directory of Open Access Journals (Sweden)

    Mailys Lopes

    2017-07-01

    Full Text Available This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the α -Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands’ management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands’ discrimination. Results are compared to other existing pixel- and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

  2. Multispectral Image Analysis for Astaxanthin Coating Classification

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht

    2012-01-01

    Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. The pellets were divided into two groups: one with pellets coated using synthetic astaxanthin in fish oil and the other with pellets coated...

  3. Classification of Agricultural Crops in Radar Images

    NARCIS (Netherlands)

    Hoogeboom, P.

    1983-01-01

    For the past few years an accurate X-band SLAR system with digital recording has been available in The Netherlands. The images of this system are corrected to indicate radar backscatter coefficients (gamma) instead of arbitrary greytones. In 1980 a radar measurement campaign was organized in the

  4. Automatic Quantification of Tumour Hypoxia From Multi-Modal Microscopy Images Using Weakly-Supervised Learning Methods.

    Science.gov (United States)

    Carneiro, Gustavo; Peng, Tingying; Bayer, Christine; Navab, Nassir

    2017-07-01

    In recently published clinical trial results, hypoxia-modified therapies have shown to provide more positive outcomes to cancer patients, compared with standard cancer treatments. The development and validation of these hypoxia-modified therapies depend on an effective way of measuring tumor hypoxia, but a standardized measurement is currently unavailable in clinical practice. Different types of manual measurements have been proposed in clinical research, but in this paper we focus on a recently published approach that quantifies the number and proportion of hypoxic regions using high resolution (immuno-)fluorescence (IF) and hematoxylin and eosin (HE) stained images of a histological specimen of a tumor. We introduce new machine learning-based methodologies to automate this measurement, where the main challenge is the fact that the clinical annotations available for training the proposed methodologies consist of the total number of normoxic, chronically hypoxic, and acutely hypoxic regions without any indication of their location in the image. Therefore, this represents a weakly-supervised structured output classification problem, where training is based on a high-order loss function formed by the norm of the difference between the manual and estimated annotations mentioned above. We propose four methodologies to solve this problem: 1) a naive method that uses a majority classifier applied on the nodes of a fixed grid placed over the input images; 2) a baseline method based on a structured output learning formulation that relies on a fixed grid placed over the input images; 3) an extension to this baseline based on a latent structured output learning formulation that uses a graph that is flexible in terms of the amount and positions of nodes; and 4) a pixel-wise labeling based on a fully-convolutional neural network. Using a data set of 89 weakly annotated pairs of IF and HE images from eight tumors, we show that the quantitative results of methods (3) and (4

  5. MAXIMUM LIKELIHOOD CLASSIFICATION OF HIGH-RESOLUTION SAR IMAGES IN URBAN AREA

    Directory of Open Access Journals (Sweden)

    M. Soheili Majd

    2012-09-01

    Full Text Available In this work, we propose a state-of-the-art on statistical analysis of polarimetric synthetic aperture radar (SAR data, through the modeling of several indices. We concentrate on eight ground classes which have been carried out from amplitudes, co-polarisation ratio, depolarization ratios, and other polarimetric descriptors. To study their different statistical behaviours, we consider Gauss, log- normal, Beta I, Weibull, Gamma, and Fisher statistical models and estimate their parameters using three methods: method of moments (MoM, maximum-likelihood (ML methodology, and log-cumulants method (MoML. Then, we study the opportunity of introducing this information in an adapted supervised classification scheme based on Maximum–Likelihood and Fisher pdf. Our work relies on an image of a suburban area, acquired by the airborne RAMSES SAR sensor of ONERA. The results prove the potential of such data to discriminate urban surfaces and show the usefulness of adapting any classical classification algorithm however classification maps present a persistant class confusion between flat gravelled or concrete roofs and trees.

  6. Classification Method in Integrated Information Network Using Vector Image Comparison

    Directory of Open Access Journals (Sweden)

    Zhou Yuan

    2014-05-01

    Full Text Available Wireless Integrated Information Network (WMN consists of integrated information that can get data from its surrounding, such as image, voice. To transmit information, large resource is required which decreases the service time of the network. In this paper we present a Classification Approach based on Vector Image Comparison (VIC for WMN that improve the service time of the network. The available methods for sub-region selection and conversion are also proposed.

  7. PHIV-RootCell: a supervised image analysis tool for rice root anatomical parameter quantification

    Directory of Open Access Journals (Sweden)

    Marc eLartaud

    2015-01-01

    Full Text Available We developed the PHIV-RootCell software to quantify anatomical traits of rice roots transverse section images. Combined with an efficient root sample processing method for image acquisition, this program permits supervised measurements of areas (those of whole root section, stele, cortex and central metaxylem vessels, number of cell layers and number of cells per cell layer. The PHIV-RootCell toolset runs under ImageJ, an independent operating system that has a license-free status. To demonstrate the usefulness of PHIV-RootCell, we conducted a genetic diversity study and an analysis of salt-stress responses of root anatomical parameters in rice (Oryza sativa L.. Using 16 cultivars, we showed that we could discriminate between some of the varieties even at the 6 day-old stage, and that tropical japonica varieties had larger root sections due to an increase in cell number. We observed, as described previously, that root sections become enlarged under salt stress. However, our results show an increase in cell number in ground tissues (endodermis and cortex but a decrease in external (peripheral tissues (sclerenchyma, exodermis and epidermis. Thus, the PHIV-RootCell program is a user-friendly tool that will be helpful for future genetic and physiological studies that investigate root anatomical trait variations.

  8. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Ricardo Andres Pizarro

    2016-12-01

    Full Text Available High-resolution three-dimensional magnetic resonance imaging (3D-MRI is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM algorithm in the quality assessment of structural brain images, using global and region of interest (ROI automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  9. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

    Science.gov (United States)

    Pizarro, Ricardo A; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A; Goldman, Aaron L; Xiao, Ena; Luo, Qian; Berman, Karen F; Callicott, Joseph H; Weinberger, Daniel R; Mattay, Venkata S

    2016-01-01

    High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  10. a Hyperspectral Image Classification Method Using Isomap and Rvm

    Science.gov (United States)

    Chang, H.; Wang, T.; Fang, H.; Su, Y.

    2018-04-01

    Classification is one of the most significant applications of hyperspectral image processing and even remote sensing. Though various algorithms have been proposed to implement and improve this application, there are still drawbacks in traditional classification methods. Thus further investigations on some aspects, such as dimension reduction, data mining, and rational use of spatial information, should be developed. In this paper, we used a widely utilized global manifold learning approach, isometric feature mapping (ISOMAP), to address the intrinsic nonlinearities of hyperspectral image for dimension reduction. Considering the impropriety of Euclidean distance in spectral measurement, we applied spectral angle (SA) for substitute when constructed the neighbourhood graph. Then, relevance vector machines (RVM) was introduced to implement classification instead of support vector machines (SVM) for simplicity, generalization and sparsity. Therefore, a probability result could be obtained rather than a less convincing binary result. Moreover, taking into account the spatial information of the hyperspectral image, we employ a spatial vector formed by different classes' ratios around the pixel. At last, we combined the probability results and spatial factors with a criterion to decide the final classification result. To verify the proposed method, we have implemented multiple experiments with standard hyperspectral images compared with some other methods. The results and different evaluation indexes illustrated the effectiveness of our method.

  11. Gaze Embeddings for Zero-Shot Image Classification

    NARCIS (Netherlands)

    Karessli, N.; Akata, Z.; Schiele, B.; Bulling, A.

    2017-01-01

    Zero-shot image classification using auxiliary information, such as attributes describing discriminative object properties, requires time-consuming annotation by domain experts. We instead propose a method that relies on human gaze as auxiliary information, exploiting that even non-expert users have

  12. Automatic segmentation and disease classification using cardiac cine MR images

    NARCIS (Netherlands)

    Wolterink, Jelmer M.; Leiner, Tim; Viergever, Max A.; Išgum, Ivana

    2018-01-01

    Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images. A convolutional neural network (CNN) was designed to simultaneously segment the left ventricle

  13. Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification.

    Science.gov (United States)

    Song, Yang; Li, Qing; Huang, Heng; Feng, Dagan; Chen, Mei; Cai, Weidong

    2017-08-01

    Microscopy image classification is important in various biomedical applications, such as cancer subtype identification, and protein localization for high content screening. To achieve automated and effective microscopy image classification, the representative and discriminative capability of image feature descriptors is essential. To this end, in this paper, we propose a new feature representation algorithm to facilitate automated microscopy image classification. In particular, we incorporate Fisher vector (FV) encoding with multiple types of local features that are handcrafted or learned, and we design a separation-guided dimension reduction method to reduce the descriptor dimension while increasing its discriminative capability. Our method is evaluated on four publicly available microscopy image data sets of different imaging types and applications, including the UCSB breast cancer data set, MICCAI 2015 CBTC challenge data set, and IICBU malignant lymphoma, and RNAi data sets. Our experimental results demonstrate the advantage of the proposed low-dimensional FV representation, showing consistent performance improvement over the existing state of the art and the commonly used dimension reduction techniques.

  14. Effects on MR images compression in tissue classification quality

    International Nuclear Information System (INIS)

    Santalla, H; Meschino, G; Ballarin, V

    2007-01-01

    It is known that image compression is required to optimize the storage in memory. Moreover, transmission speed can be significantly improved. Lossless compression is used without controversy in medicine, though benefits are limited. If we compress images lossy, where image can not be totally recovered; we can only recover an approximation. In this point definition of 'quality' is essential. What we understand for 'quality'? How can we evaluate a compressed image? Quality in images is an attribute whit several definitions and interpretations, which actually depend on the posterior use we want to give them. This work proposes a quantitative analysis of quality for lossy compressed Magnetic Resonance (MR) images, and their influence in automatic tissue classification, accomplished with these images

  15. Magnetic resonance imaging texture analysis classification of primary breast cancer

    International Nuclear Information System (INIS)

    Waugh, S.A.; Lerski, R.A.; Purdie, C.A.; Jordan, L.B.; Vinnicombe, S.; Martin, P.; Thompson, A.M.

    2016-01-01

    Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

  16. Magnetic resonance imaging texture analysis classification of primary breast cancer

    Energy Technology Data Exchange (ETDEWEB)

    Waugh, S.A.; Lerski, R.A. [Ninewells Hospital and Medical School, Department of Medical Physics, Dundee (United Kingdom); Purdie, C.A.; Jordan, L.B. [Ninewells Hospital and Medical School, Department of Pathology, Dundee (United Kingdom); Vinnicombe, S. [University of Dundee, Division of Imaging and Technology, Ninewells Hospital and Medical School, Dundee (United Kingdom); Martin, P. [Ninewells Hospital and Medical School, Department of Clinical Radiology, Dundee (United Kingdom); Thompson, A.M. [University of Texas MD Anderson Cancer Center, Department of Surgical Oncology, Houston, TX (United States)

    2016-02-15

    Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

  17. Feature-space transformation improves supervised segmentation across scanners

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Achterberg, Hakim C.; de Bruijne, Marleen

    2015-01-01

    Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions...

  18. Image Classification Based on Convolutional Denoising Sparse Autoencoder

    Directory of Open Access Journals (Sweden)

    Shuangshuang Chen

    2017-01-01

    Full Text Available Image classification aims to group images into corresponding semantic categories. Due to the difficulties of interclass similarity and intraclass variability, it is a challenging issue in computer vision. In this paper, an unsupervised feature learning approach called convolutional denoising sparse autoencoder (CDSAE is proposed based on the theory of visual attention mechanism and deep learning methods. Firstly, saliency detection method is utilized to get training samples for unsupervised feature learning. Next, these samples are sent to the denoising sparse autoencoder (DSAE, followed by convolutional layer and local contrast normalization layer. Generally, prior in a specific task is helpful for the task solution. Therefore, a new pooling strategy—spatial pyramid pooling (SPP fused with center-bias prior—is introduced into our approach. Experimental results on the common two image datasets (STL-10 and CIFAR-10 demonstrate that our approach is effective in image classification. They also demonstrate that none of these three components: local contrast normalization, SPP fused with center-prior, and l2 vector normalization can be excluded from our proposed approach. They jointly improve image representation and classification performance.

  19. The Royal College of Radiologists Breast Group breast imaging classification

    International Nuclear Information System (INIS)

    Maxwell, A.J.; Ridley, N.T.; Rubin, G.; Wallis, M.G.; Gilbert, F.J.; Michell, M.J.

    2009-01-01

    Standardisation of the classification of breast imaging reports will improve communication between the referrer and the radiologist and avoid ambiguity, which may otherwise lead to mismanagement of patients. Following wide consultation, Royal College of Radiologists Breast Group has produced a scoring system for the classification of breast imaging. This will facilitate audit and the development of nationally agreed standards for the investigation of women with breast disease. This five-point system is as follows: 1, normal; 2, benign findings; 3, indeterminate/probably benign findings; 4, findings suspicious of malignancy; 5, findings highly suspicious of malignancy. It is recommended that this be used in the reporting of all breast imaging examinations in the UK.

  20. Automatic Segmentation of Dermoscopic Images by Iterative Classification

    Directory of Open Access Journals (Sweden)

    Maciel Zortea

    2011-01-01

    Full Text Available Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

  1. Multiview vector-valued manifold regularization for multilabel image classification.

    Science.gov (United States)

    Luo, Yong; Tao, Dacheng; Xu, Chang; Xu, Chao; Liu, Hong; Wen, Yonggang

    2013-05-01

    In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g., pedestrian, bicycle, and tree) and is properly characterized by multiple visual features (e.g., color, texture, and shape). Currently, available tools ignore either the label relationship or the view complementarily. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multilabel structure in the output space, we introduce multiview vector-valued manifold regularization (MV(3)MR) to integrate multiple features. MV(3)MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conduct extensive experiments on two challenging, but popular, datasets, PASCAL VOC' 07 and MIR Flickr, and validate the effectiveness of the proposed MV(3)MR for image classification.

  2. Supervised target detection in hyperspectral images using one-class Fukunaga-Koontz Transform

    Science.gov (United States)

    Binol, Hamidullah; Bal, Abdullah

    2016-05-01

    A novel hyperspectral target detection technique based on Fukunaga-Koontz transform (FKT) is presented. FKT offers significant properties for feature selection and ordering. However, it can only be used to solve multi-pattern classification problems. Target detection may be considered as a two-class classification problem, i.e., target versus background clutter. Nevertheless, background clutter typically contains different types of materials. That's why; target detection techniques are different than classification methods by way of modeling clutter. To avoid the modeling of the background clutter, we have improved one-class FKT (OC-FKT) for target detection. The statistical properties of target training samples are used to define tunnel-like boundary of the target class. Non-target samples are then created synthetically as to be outside of the boundary. Thus, only limited target samples become adequate for training of FKT. The hyperspectral image experiments confirm that the proposed OC-FKT technique provides an effective means for target detection.

  3. A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data.

    Directory of Open Access Journals (Sweden)

    David Stephens

    Full Text Available Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i the two primary features of bathymetry and backscatter, ii a subset of the features chosen by a feature selection process and iii all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well

  4. G0-WISHART Distribution Based Classification from Polarimetric SAR Images

    Science.gov (United States)

    Hu, G. C.; Zhao, Q. H.

    2017-09-01

    Enormous scientific and technical developments have been carried out to further improve the remote sensing for decades, particularly Polarimetric Synthetic Aperture Radar(PolSAR) technique, so classification method based on PolSAR images has getted much more attention from scholars and related department around the world. The multilook polarmetric G0-Wishart model is a more flexible model which describe homogeneous, heterogeneous and extremely heterogeneous regions in the image. Moreover, the polarmetric G0-Wishart distribution dose not include the modified Bessel function of the second kind. It is a kind of simple statistical distribution model with less parameter. To prove its feasibility, a process of classification has been tested with the full-polarized Synthetic Aperture Radar (SAR) image by the method. First, apply multilook polarimetric SAR data process and speckle filter to reduce speckle influence for classification result. Initially classify the image into sixteen classes by H/A/α decomposition. Using the ICM algorithm to classify feature based on the G0-Wshart distance. Qualitative and quantitative results show that the proposed method can classify polaimetric SAR data effectively and efficiently.

  5. Supervised Classification in the Presence of Misclassified Training Data: A Monte Carlo Simulation Study in the Three Group Case

    Directory of Open Access Journals (Sweden)

    Jocelyn E Bolin

    2014-02-01

    Full Text Available Statistical classification of phenomena into observed groups is very common in the social and behavioral sciences. Statistical classification methods, however, are affected by the characteristics of the data under study. Statistical classification can be further complicated by initial misclassification of the observed groups. The purpose of this study is to investigate the impact of initial training data misclassification on several statistical classification and data mining techniques. Misclassification conditions in the three-group case will be simulated and results will be presented in terms of overall as well as subgroup classification accuracy. Results show decreased classification accuracy as sample size, group separation and group size ratio decrease and as misclassification percentage increases with random forests demonstrating the highest accuracy across conditions.

  6. Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

    OpenAIRE

    Nahid, Abdullah-Al; Kong, Yinan

    2017-01-01

    Breast cancer is one of the largest causes of women’s death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors’ and physicians’ time. Despite the various publications on breast image classification, very few review papers are available w...

  7. Malignant fatty tumors: classification, clinical course, imaging appearance and treatment

    International Nuclear Information System (INIS)

    Peterson, J.J.; Kransdorf, M.J.; Bancroft, L.W.; O'Connor, M.I.

    2003-01-01

    Liposarcoma is a relatively common soft tissue malignancy with a wide spectrum of clinical presentations and imaging appearances. Several subtypes are described, ranging from lesions nearly entirely composed of mature adipose tissue, to tumors with very sparse adipose elements. The imaging appearance of these fatty masses is frequently sufficiently characteristic to allow a specific diagnosis, while in other cases, although a specific diagnosis is not achievable, a meaningful limited differential diagnosis can be established. The purpose of this paper is to review the spectrum of malignant fatty tumors, highlighting the current classification system, clinical presentation and behavior, treatment and spectrum of imaging appearances. The imaging review will emphasize CT scanning and MR imaging, and will stress differentiating radiologic features. (orig.)

  8. Benign fatty tumors: classification, clinical course, imaging appearance, and treatment

    International Nuclear Information System (INIS)

    Bancroft, Laura W.; Kransdorf, Mark J.; Peterson, Jeffrey J.; O'Connor, Mary I.

    2006-01-01

    Lipoma is the most common soft-tissue tumor, with a wide spectrum of clinical presentations and imaging appearances. Several subtypes are described, ranging from lesions entirely composed of mature adipose tissue to tumors intimately associated with nonadipose tissue, to those composed of brown fat. The imaging appearance of these fatty masses is frequently sufficiently characteristic to allow a specific diagnosis. However, in other cases, although a specific diagnosis is not achievable, a meaningful limited differential diagnosis can be established. The purpose of this manuscript is to review the spectrum of benign fatty tumors highlighting the current classification system, clinical presentation and behavior, spectrum of imaging appearances, and treatment. The imaging review emphasizes computed tomography (CT) scanning and magnetic resonance (MR) imaging, differentiating radiologic features. (orig.)

  9. Fermat's principle, caustics, and the classification of gravitational lens images

    International Nuclear Information System (INIS)

    Blandford, R.; Narayan, R.

    1986-01-01

    A scalar description of gravitational lensing based on Fermat's principle is described. The lensing mass is assumed to be confined to a single plane between the source and the observer, and a time delay is associated with each position in the sky of a potential image. The extrema of this time surface then give the true positions of the images. A topological classification of image configurations is presented, and the results are generalized to cases of three and five-image lensing geometries. A computer-graphical approach to the study of lensing by model galaxies and clusters is described, and the design of a simple optical apparatus which could be used for fast modelling of image geometries is outlined. The connection between the Fermat approach and the classical theory of caustics and the more recent general theory of catastrophies is developed. The extension of the results to multiple scattering is considered. 42 references

  10. A SURVEY OF SEMI-SUPERVISED LEARNING

    OpenAIRE

    Amrita Sadarangani *, Dr. Anjali Jivani

    2016-01-01

    Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for clustering. Semi supervised learning finds usage in many applications, since labeled data can be hard to find in many cases. Currently, a lot of research is being conducted in this area. This paper discusses the different algorithms of semi supervised learning and then their advantages and limitations are compared. The differences between supervised classification and semi-supervised classific...

  11. Automated retinal vessel type classification in color fundus images

    Science.gov (United States)

    Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.

    2013-02-01

    Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

  12. Quantitative analysis and classification of AFM images of human hair.

    Science.gov (United States)

    Gurden, S P; Monteiro, V F; Longo, E; Ferreira, M M C

    2004-07-01

    The surface topography of human hair, as defined by the outer layer of cellular sheets, termed cuticles, largely determines the cosmetic properties of the hair. The condition of the cuticles is of great cosmetic importance, but also has the potential to aid diagnosis in the medical and forensic sciences. Atomic force microscopy (AFM) has been demonstrated to offer unique advantages for analysis of the hair surface, mainly due to the high image resolution and the ease of sample preparation. This article presents an algorithm for the automatic analysis of AFM images of human hair. The cuticular structure is characterized using a series of descriptors, such as step height, tilt angle and cuticle density, allowing quantitative analysis and comparison of different images. The usefulness of this approach is demonstrated by a classification study. Thirty-eight AFM images were measured, consisting of hair samples from (a) untreated and bleached hair samples, and (b) the root and distal ends of the hair fibre. The multivariate classification technique partial least squares discriminant analysis is used to test the ability of the algorithm to characterize the images according to the properties of the hair samples. Most of the images (86%) were found to be classified correctly.

  13. Segmentation and classification of cell cycle phases in fluorescence imaging.

    Science.gov (United States)

    Ersoy, Ilker; Bunyak, Filiz; Chagin, Vadim; Cardoso, M Christina; Palaniappan, Kannappan

    2009-01-01

    Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

  14. Image Representation using Consensus Vocabulary and Food Images Classification

    OpenAIRE

    Moltisanti, Marco

    2016-01-01

    Digital images are the result of many physical factors, such as illumination, point of view an thermal noise of the sensor. These elements may be irrelevant for a specific Computer Vision task; for instance, in the object detection task, the viewpoint and the color of the object should not be relevant in order to answer the question "Is the object present in the image?". Nevertheless, an image depends crucially on all such parameters and it is simply not possible to ignore them in analysis. H...

  15. A non-parametric, supervised classification of vegetation types on the Kaibab National Forest using decision trees

    Science.gov (United States)

    Suzanne M. Joy; R. M. Reich; Richard T. Reynolds

    2003-01-01

    Traditional land classification techniques for large areas that use Landsat Thematic Mapper (TM) imagery are typically limited to the fixed spatial resolution of the sensors (30m). However, the study of some ecological processes requires land cover classifications at finer spatial resolutions. We model forest vegetation types on the Kaibab National Forest (KNF) in...

  16. A comparative study of deep learning models for medical image classification

    Science.gov (United States)

    Dutta, Suvajit; Manideep, B. C. S.; Rai, Shalva; Vijayarajan, V.

    2017-11-01

    Deep Learning(DL) techniques are conquering over the prevailing traditional approaches of neural network, when it comes to the huge amount of dataset, applications requiring complex functions demanding increase accuracy with lower time complexities. Neurosciences has already exploited DL techniques, thus portrayed itself as an inspirational source for researchers exploring the domain of Machine learning. DL enthusiasts cover the areas of vision, speech recognition, motion planning and NLP as well, moving back and forth among fields. This concerns with building models that can successfully solve variety of tasks requiring intelligence and distributed representation. The accessibility to faster CPUs, introduction of GPUs-performing complex vector and matrix computations, supported agile connectivity to network. Enhanced software infrastructures for distributed computing worked in strengthening the thought that made researchers suffice DL methodologies. The paper emphases on the following DL procedures to traditional approaches which are performed manually for classifying medical images. The medical images are used for the study Diabetic Retinopathy(DR) and computed tomography (CT) emphysema data. Both DR and CT data diagnosis is difficult task for normal image classification methods. The initial work was carried out with basic image processing along with K-means clustering for identification of image severity levels. After determining image severity levels ANN has been applied on the data to get the basic classification result, then it is compared with the result of DNNs (Deep Neural Networks), which performed efficiently because of its multiple hidden layer features basically which increases accuracy factors, but the problem of vanishing gradient in DNNs made to consider Convolution Neural Networks (CNNs) as well for better results. The CNNs are found to be providing better outcomes when compared to other learning models aimed at classification of images. CNNs are

  17. Classification of ADHD children through multimodal Magnetic Resonance Imaging

    Directory of Open Access Journals (Sweden)

    Dai eDai

    2012-09-01

    Full Text Available Attention deficit/hyperactivity disorder (ADHD is one of the most common diseases in school-age children. To date, the diagnosis of ADHD is mainly subjective and studies of objective diagnostic method are of great importance. Although many efforts have been made recently to investigate the use of structural and functional brain images for the diagnosis purpose, few of them are related to ADHD. In this paper, we introduce an automatic classification framework based on brain imaging features of ADHD patients, and present in detail the feature extraction, feature selection and classifier training methods. The effects of using different features are compared against each other. In addition, we integrate multimodal image features using multi-kernel learning (MKL. The performance of our framework has been validated in the ADHD-200 Global Competition, which is a world-wide classification contest on the ADHD-200 datasets. In this competition, our classification framework using features of resting-state functional connectivity was ranked the 6th out of 21 participants under the competition scoring policy, and performed the best in terms of sensitivity and J-statistic.

  18. Multiview Discriminative Geometry Preserving Projection for Image Classification

    Directory of Open Access Journals (Sweden)

    Ziqiang Wang

    2014-01-01

    Full Text Available In many image classification applications, it is common to extract multiple visual features from different views to describe an image. Since different visual features have their own specific statistical properties and discriminative powers for image classification, the conventional solution for multiple view data is to concatenate these feature vectors as a new feature vector. However, this simple concatenation strategy not only ignores the complementary nature of different views, but also ends up with “curse of dimensionality.” To address this problem, we propose a novel multiview subspace learning algorithm in this paper, named multiview discriminative geometry preserving projection (MDGPP for feature extraction and classification. MDGPP can not only preserve the intraclass geometry and interclass discrimination information under a single view, but also explore the complementary property of different views to obtain a low-dimensional optimal consensus embedding by using an alternating-optimization-based iterative algorithm. Experimental results on face recognition and facial expression recognition demonstrate the effectiveness of the proposed algorithm.

  19. Agricultural crop mapping and classification by Landsat images to evaluate water use in the Lake Urmia basin, North-west Iran

    Science.gov (United States)

    Fazel, Nasim; Norouzi, Hamid; Madani, Kaveh; Kløve, Bjørn

    2016-04-01

    Lake Urmia, once one of the largest hypersaline lakes in the world has lost more than 90% of its surface body mainly due to the intensive expansion of agriculture, using more than 90% of all water in the region. Access to accurate and up-to-date information on the extent and distribution of individual crop types, associated with land use changes and practices, has significant value in intensively agricultural regions. Explicit information of croplands can be useful for sustainable water resources, land and agriculture planning and management. Remote sensing, has been proven to be a more cost-effective alternative to the traditional statistically-based ground surveys for crop coverage areas that are costly and provide insufficient information. Satellite images along with ground surveys can provide the necessary information of spatial coverage and spectral responses of croplands for sustainable agricultural management. This study strives to differentiate different crop types and agricultural practices to achieve a higher detailed crop map of the Lake Urmia basin. The mapping approach consists of a two-stage supervised classification of multi-temporal multi-spectral high resolution images obtained from Landsat imagery archive. Irrigated and non-irrigated croplands and orchards were separated from other major land covers (urban, ranges, bare-lands, and water) in the region by means of maximum Likelihood supervised classification method. The field data collected during 2015 and land use maps generated in 2007 and Google Earth comparisons were used to form a training data set to perform the supervised classification. In the second stage, non-agricultural lands were masked and the supervised classification was applied on the Landsat images stack to identify seven major croplands in the region (wheat and barley, beetroot, corn, sunflower, alfalfa, vineyards, and apple orchards). The obtained results can be of significant value to the Urmia Lake restoration efforts which

  20. Towards an efficient and robust foot classification from pedobarographic images

    OpenAIRE

    Oliveira, Francisco; Sousa, Andreia S. P.; Santos, Rubim; Tavares, João Manuel

    2012-01-01

    O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor). This paper presents a new computational framework for automatic foot classification from digital plantar pressure images. It classifies the foot as left or right and simultaneously calculates two well-known footprint indices: the Cavanagh's arch index and the modified arch index. The accuracy of the framework was evaluated using a set of plantar pressure images from two common pedobarographic devices. The...

  1. Interactive classification and content-based retrieval of tissue images

    Science.gov (United States)

    Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

    2002-11-01

    We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

  2. Histological image classification using biologically interpretable shape-based features

    International Nuclear Information System (INIS)

    Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

    2013-01-01

    Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

  3. Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

    KAUST Repository

    Zhu, Xiaofeng; Xie, Qing; Zhu, Yonghua; Liu, Xingyi; Zhang, Shichao

    2015-01-01

    This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple

  4. Imaging of juvenile spondyloarthritis. Part I: Classifications and radiographs

    Directory of Open Access Journals (Sweden)

    Iwona Sudoł-Szopińska

    2017-09-01

    Full Text Available Juvenile spondyloarthropathies are manifested mainly by symptoms of peripheral arthritis and enthesitis. By contrast with adults, children rarely present with sacroiliitis and spondylitis. Imaging and laboratory tests allow early diagnosis and treatment. Conventional radiographs visualize late inflammatory lesions and post-inflammatory complications. Early diagnosis is possible with the use of ultrasonography and magnetic resonance imaging. The first part of the article presents classifications of juvenile spondyloarthropathies and discusses their radiographic presentation. Typical radiographic features of individual types of juvenile spondyloarthritis are listed (including ankylosing spondylitis, juvenile psoriatic arthritis, reactive arthritis and arthritis in the course of inflammatory bowel diseases. The second part will describe changes visible on ultrasonography and magnetic resonance imaging. In patients with juvenile spondyloarthropathies, these examinations are conducted to diagnose inflammatory lesions in peripheral joints, tendon sheaths, tendons and bursae. Moreover, magnetic resonance imaging also visualizes early inflammatory changes in the axial skeleton and subchondral bone marrow edema, which is considered an early sign of inflammation.

  5. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Yidong Tang

    2016-01-01

    Full Text Available The sparse representation based classifier (SRC and its kernel version (KSRC have been employed for hyperspectral image (HSI classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

  6. Blood vessel classification into arteries and veins in retinal images

    Science.gov (United States)

    Kondermann, Claudia; Kondermann, Daniel; Yan, Michelle

    2007-03-01

    The prevalence of diabetes is expected to increase dramatically in coming years; already today it accounts for a major proportion of the health care budget in many countries. Diabetic Retinopathy (DR), a micro vascular complication very often seen in diabetes patients, is the most common cause of visual loss in working age population of developed countries today. Since the possibility of slowing or even stopping the progress of this disease depends on the early detection of DR, an automatic analysis of fundus images would be of great help to the ophthalmologist due to the small size of the symptoms and the large number of patients. An important symptom for DR are abnormally wide veins leading to an unusually low ratio of the average diameter of arteries to veins (AVR). There are also other diseases like high blood pressure or diseases of the pancreas with one symptom being an abnormal AVR value. To determine it, a classification of vessels as arteries or veins is indispensable. As to our knowledge despite the importance there have only been two approaches to vessel classification yet. Therefore we propose an improved method. We compare two feature extraction methods and two classification methods based on support vector machines and neural networks. Given a hand-segmentation of vessels our approach achieves 95.32% correctly classified vessel pixels. This value decreases by 10% on average, if the result of a segmentation algorithm is used as basis for the classification.

  7. Classification of breast cancer histology images using Convolutional Neural Networks.

    Directory of Open Access Journals (Sweden)

    Teresa Araújo

    Full Text Available Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

  8. Feature Importance for Human Epithelial (HEp-2 Cell Image Classification

    Directory of Open Access Journals (Sweden)

    Vibha Gupta

    2018-02-01

    Full Text Available Indirect Immuno-Fluorescence (IIF microscopy imaging of human epithelial (HEp-2 cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods. Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

  9. CLASSIFICATION AND RECOGNITION OF TOMB INFORMATION IN HYPERSPECTRAL IMAGE

    Directory of Open Access Journals (Sweden)

    M. Gu

    2018-04-01

    Full Text Available There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.

  10. Tongue Images Classification Based on Constrained High Dispersal Network

    Directory of Open Access Journals (Sweden)

    Dan Meng

    2017-01-01

    Full Text Available Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM. However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN, we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

  11. Classification and Recognition of Tomb Information in Hyperspectral Image

    Science.gov (United States)

    Gu, M.; Lyu, S.; Hou, M.; Ma, S.; Gao, Z.; Bai, S.; Zhou, P.

    2018-04-01

    There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.

  12. Study and development of equipment supervision technique system and its management software for nuclear electricity production

    International Nuclear Information System (INIS)

    Zhang Liying; Zou Pingguo; Zhu Chenghu; Lu Haoliang; Wu Jie

    2008-01-01

    The equipment supervision technique system, which standardized the behavior of supervision organizations in planning and implementing of equipment supervision, is built up based on equipment supervision technique documents, such as Quality Supervision Classifications, Special Supervision Plans and Supervision Guides. Furthermore, based on the research, the equipment supervision management information system is developed by Object Oriented Programming, which consists of supervision information, supervision technique, supervision implementation, quality statistics and analysis module. (authors)

  13. Kollegial supervision

    DEFF Research Database (Denmark)

    Andersen, Ole Dibbern; Petersson, Erling

    Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution......Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution...

  14. Classification of bacterial contamination using image processing and distributed computing.

    Science.gov (United States)

    Ahmed, W M; Bayraktar, B; Bhunia, A; Hirleman, E D; Robinson, J P; Rajwa, B

    2013-01-01

    Disease outbreaks due to contaminated food are a major concern not only for the food-processing industry but also for the public at large. Techniques for automated detection and classification of microorganisms can be a great help in preventing outbreaks and maintaining the safety of the nations food supply. Identification and classification of foodborne pathogens using colony scatter patterns is a promising new label-free technique that utilizes image-analysis and machine-learning tools. However, the feature-extraction tools employed for this approach are computationally complex, and choosing the right combination of scatter-related features requires extensive testing with different feature combinations. In the presented work we used computer clusters to speed up the feature-extraction process, which enables us to analyze the contribution of different scatter-based features to the overall classification accuracy. A set of 1000 scatter patterns representing ten different bacterial strains was used. Zernike and Chebyshev moments as well as Haralick texture features were computed from the available light-scatter patterns. The most promising features were first selected using Fishers discriminant analysis, and subsequently a support-vector-machine (SVM) classifier with a linear kernel was used. With extensive testing we were able to identify a small subset of features that produced the desired results in terms of classification accuracy and execution speed. The use of distributed computing for scatter-pattern analysis, feature extraction, and selection provides a feasible mechanism for large-scale deployment of a light scatter-based approach to bacterial classification.

  15. Automated processing of webcam images for phenological classification.

    Science.gov (United States)

    Bothmann, Ludwig; Menzel, Annette; Menze, Bjoern H; Schunk, Christian; Kauermann, Göran

    2017-01-01

    Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels' time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the statistical software

  16. Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems

    International Nuclear Information System (INIS)

    Traore, M.; Chammas, A.; Duviella, E.

    2015-01-01

    In this paper, we are concerned by the improvement of the safety, availability and reliability of dynamical systems’ components subjected to slow degradations (slow drifts). We propose an architecture for efficient Predictive Maintenance (PM) according to the real time estimate of the future state of the components. The architecture is built on supervision and prognosis tools. The prognosis method is based on an appropriated supervision technique that consists in drift tracking of the dynamical systems using AUDyC (AUto-adaptive and Dynamical Clustering), that is an auto-adaptive dynamical classifier. Thus, due to the complexity and the dynamical of the considered systems, the Failure Mode Effect and Criticity Analysis (FMECA) is used to identify the key components of the systems. A component is defined as an element of the system that can be impacted by only one failure. A failure of a key component causes a long downtime of the system. From the FMECA, a Fault Tree Analysis (FTA) of the system are built to determine the propagation laws of a failure on the system by using a deductive method. The proposed architecture is implemented for the PM of a thermoregulator. The application on this real system highlights the interests and the performances of the proposed architecture

  17. Accessory cardiac bronchus: Proposed imaging classification on multidetector CT

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Kang Min; Kim, Young Tong; Han, Jong Kyu; Jou, Sung Shick [Dept. of Radiology, Soonchunhyang University College of Medicine, Cheonan Hospital, Cheonan (Korea, Republic of)

    2016-02-15

    To propose the classification of accessory cardiac bronchus (ACB) based on imaging using multidetector computed tomography (MDCT), and evaluate follow-up changes of ACB. This study included 58 patients diagnosed as ACB since 9 years, using MDCT. We analyzed the types, division locations and division directions of ACB, and also evaluated changes on follow-up. We identified two main types of ACB: blind-end (51.7%) and lobule (48.3%). The blind-end ACB was further classified into three subtypes: blunt (70%), pointy (23.3%) and saccular (6.7%). The lobule ACB was also further classified into three subtypes: complete (46.4%), incomplete (28.6%) and rudimentary (25%). Division location to the upper half bronchus intermedius (79.3%) and medial direction (60.3%) were the most common in all patients. The difference in division direction was statistically significant between the blind-end and lobule types (p = 0.019). Peribronchial soft tissue was found in five cases. One calcification case was identified in the lobule type. During follow-up, ACB had disappeared in two cases of the blind-end type and in one case of the rudimentary subtype. The proposed classification of ACB based on imaging, and the follow-up CT, helped us to understand the various imaging features of ACB.

  18. Classification in medical images using adaptive metric k-NN

    Science.gov (United States)

    Chen, C.; Chernoff, K.; Karemore, G.; Lo, P.; Nielsen, M.; Lauze, F.

    2010-03-01

    The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.

  19. Early differential processing of material images: Evidence from ERP classification.

    Science.gov (United States)

    Wiebel, Christiane B; Valsecchi, Matteo; Gegenfurtner, Karl R

    2014-06-24

    Investigating the temporal dynamics of natural image processing using event-related potentials (ERPs) has a long tradition in object recognition research. In a classical Go-NoGo task two characteristic effects have been emphasized: an early task independent category effect and a later task-dependent target effect. Here, we set out to use this well-established Go-NoGo paradigm to study the time course of material categorization. Material perception has gained more and more interest over the years as its importance in natural viewing conditions has been ignored for a long time. In addition to analyzing standard ERPs, we conducted a single trial ERP pattern analysis. To validate this procedure, we also measured ERPs in two object categories (people and animals). Our linear classification procedure was able to largely capture the overall pattern of results from the canonical analysis of the ERPs and even extend it. We replicate the known target effect (differential Go-NoGo potential at frontal sites) for the material images. Furthermore, we observe task-independent differential activity between the two material categories as early as 140 ms after stimulus onset. Using our linear classification approach, we show that material categories can be differentiated consistently based on the ERP pattern in single trials around 100 ms after stimulus onset, independent of the target-related status. This strengthens the idea of early differential visual processing of material categories independent of the task, probably due to differences in low-level image properties and suggests pattern classification of ERP topographies as a strong instrument for investigating electrophysiological brain activity. © 2014 ARVO.

  20. A kernel-based multi-feature image representation for histopathology image classification

    International Nuclear Information System (INIS)

    Moreno J; Caicedo J Gonzalez F

    2010-01-01

    This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of latent semantic analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, support vector machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that; the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

  1. A KERNEL-BASED MULTI-FEATURE IMAGE REPRESENTATION FOR HISTOPATHOLOGY IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    J Carlos Moreno

    2010-09-01

    Full Text Available This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

  2. A method to incorporate uncertainty in the classification of remote sensing images

    OpenAIRE

    Gonçalves, Luísa M. S.; Fonte, Cidália C.; Júlio, Eduardo N. B. S.; Caetano, Mario

    2009-01-01

    The aim of this paper is to investigate if the incorporation of the uncertainty associated with the classification of surface elements into the classification of landscape units (LUs) increases the results accuracy. To this end, a hybrid classification method is developed, including uncertainty information in the classification of very high spatial resolution multi-spectral satellite images, to obtain a map of LUs. The developed classification methodology includes the following...

  3. Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana

    Science.gov (United States)

    Bayoudh, Meriam; Roux, Emmanuel; Richard, Gilles; Nock, Richard

    2015-03-01

    The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.

  4. Chondrosarcoma : MR imaging findings correlated with pathologic classification and grade

    International Nuclear Information System (INIS)

    Cho, Seong Whi; Kang, Heung Sik; Kim, Sam Soo; Lee, Sang Hyun; Cho, Jeong Yeon; Yeon, Kyung Mo

    1996-01-01

    To evaluate the MR imaging findings of chondrosarcomas by correlation with pathologic classification and grade. We performed MR imaging-pathologic correlation of nineteen chondrosarcomas. Conventional chondrosarcomas accounted for 15 cases (grade I:6, II:6, III:3) and the mesenchymal and dedifferentiated types each accounted for two. MR signal intensity (SI) of the tumor on T1- and T2-weighted images (T1WI and T2WI, respectively), was classified as homogeneous or heterogeneous low-, iso- or high SI, and enhancing pattern as marginal, marginal and septal, marginal and nodular, or diffuse enhancement. Eighteen cases of chondrosarcomas (95%) showed homogeneous or heterogeneous low- or iso SI on T1WI and high SI on T2WI. Low grade conventional chondrosarcomas showed marginal and septal (n=8/10) or marginal (n=2/10) enhancement on Gd-enhanced MR images. Grade III conventional chondrosarcomas showed marginal or marginal and nodular enhancement. Dedifferentiated and mesenchymal chondrosarcomas showed marginal and nodular or diffuse enhancement. Chondrosarcomas showed iso- or low SI on T1WI and high SI on T2WI. Marginal and septal enhancement was demonstrated on Gd-enhanced MR images of grade I and II conventional chondrosarcomas. If a tumor showed a marginal and nodular or diffuse enhancing pattern, this suggested it was a of high grade chondrosarcoma

  5. Recurrent Neural Networks to Correct Satellite Image Classification Maps

    Science.gov (United States)

    Maggiori, Emmanuel; Charpiat, Guillaume; Tarabalka, Yuliya; Alliez, Pierre

    2017-09-01

    While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them good at recognizing but poor at localizing objects precisely. This problem is magnified in the context of aerial and satellite image labeling, where a spatially fine object outlining is of paramount importance. Different iterative enhancement algorithms have been presented in the literature to progressively improve the coarse CNN outputs, seeking to sharpen object boundaries around real image edges. However, one must carefully design, choose and tune such algorithms. Instead, our goal is to directly learn the iterative process itself. For this, we formulate a generic iterative enhancement process inspired from partial differential equations, and observe that it can be expressed as a recurrent neural network (RNN). Consequently, we train such a network from manually labeled data for our enhancement task. In a series of experiments we show that our RNN effectively learns an iterative process that significantly improves the quality of satellite image classification maps.

  6. MR imaging-based diagnosis and classification of meniscal tears.

    Science.gov (United States)

    Nguyen, Jie C; De Smet, Arthur A; Graf, Ben K; Rosas, Humberto G

    2014-01-01

    Magnetic resonance (MR) imaging is currently the modality of choice for detecting meniscal injuries and planning subsequent treatment. A thorough understanding of the imaging protocols, normal meniscal anatomy, surrounding anatomic structures, and anatomic variants and pitfalls is critical to ensure diagnostic accuracy and prevent unnecessary surgery. High-spatial-resolution imaging of the meniscus can be performed using fast spin-echo and three-dimensional MR imaging sequences. Normal anatomic structures that can mimic a tear include the meniscal ligament, meniscofemoral ligaments, popliteomeniscal fascicles, and meniscomeniscal ligament. Anatomic variants and pitfalls that can mimic a tear include discoid meniscus, meniscal flounce, a meniscal ossicle, and chondrocalcinosis. When a meniscal tear is identified, accurate description and classification of the tear pattern can guide the referring clinician in patient education and surgical planning. For example, longitudinal tears are often amenable to repair, whereas horizontal and radial tears may require partial meniscectomy. Tear patterns include horizontal, longitudinal, radial, root, complex, displaced, and bucket-handle tears. Occasionally, meniscal tears can be difficult to detect at imaging; however, secondary indirect signs, such as a parameniscal cyst, meniscal extrusion, or linear subchondral bone marrow edema, should increase the radiologist's suspicion for an underlying tear. Awareness of common diagnostic errors can ensure accurate diagnosis of meniscal tears. Online supplemental material is available for this article. ©RSNA, 2014.

  7. Superpixel-based classification of gastric chromoendoscopy images

    Science.gov (United States)

    Boschetto, Davide; Grisan, Enrico

    2017-03-01

    Chromoendoscopy (CH) is a gastroenterology imaging modality that involves the staining of tissues with methylene blue, which reacts with the internal walls of the gastrointestinal tract, improving the visual contrast in mucosal surfaces and thus enhancing a doctor's ability to screen precancerous lesions or early cancer. This technique helps identify areas that can be targeted for biopsy or treatment and in this work we will focus on gastric cancer detection. Gastric chromoendoscopy for cancer detection has several taxonomies available, one of which classifies CH images into three classes (normal, metaplasia, dysplasia) based on color, shape and regularity of pit patterns. Computer-assisted diagnosis is desirable to help us improve the reliability of the tissue classification and abnormalities detection. However, traditional computer vision methodologies, mainly segmentation, do not translate well to the specific visual characteristics of a gastroenterology imaging scenario. We propose the exploitation of a first unsupervised segmentation via superpixel, which groups pixels into perceptually meaningful atomic regions, used to replace the rigid structure of the pixel grid. For each superpixel, a set of features is extracted and then fed to a random forest based classifier, which computes a model used to predict the class of each superpixel. The average general accuracy of our model is 92.05% in the pixel domain (86.62% in the superpixel domain), while detection accuracies on the normal and abnormal class are respectively 85.71% and 95%. Eventually, the whole image class can be predicted image through a majority vote on each superpixel's predicted class.

  8. Towards an efficient and robust foot classification from pedobarographic images.

    Science.gov (United States)

    Oliveira, Francisco P M; Sousa, Andreia; Santos, Rubim; Tavares, João Manuel R S

    2012-01-01

    This paper presents a new computational framework for automatic foot classification from digital plantar pressure images. It classifies the foot as left or right and simultaneously calculates two well-known footprint indices: the Cavanagh's arch index (AI) and the modified AI. The accuracy of the framework was evaluated using a set of plantar pressure images from two common pedobarographic devices. The results were outstanding, as all feet under analysis were correctly classified as left or right and no significant differences were observed between the footprint indices calculated using the computational solution and the traditional manual method. The robustness of the proposed framework to arbitrary foot orientations and to the acquisition device was also tested and confirmed.

  9. Rapid hyperspectral image classification to enable autonomous search systems

    Directory of Open Access Journals (Sweden)

    Raj Bridgelal

    2016-11-01

    Full Text Available The emergence of lightweight full-frame hyperspectral cameras is destined to enable autonomous search vehicles in the air, on the ground and in water. Self-contained and long-endurance systems will yield important new applications, for example, in emergency response and the timely identification of environmental hazards. One missing capability is rapid classification of hyperspectral scenes so that search vehicles can immediately take actions to verify potential targets. Onsite verifications minimise false positives and preclude the expense of repeat missions. Verifications will require enhanced image quality, which is achievable by either moving closer to the potential target or by adjusting the optical system. Such a solution, however, is currently impractical for small mobile platforms with finite energy sources. Rapid classifications with current methods demand large computing capacity that will quickly deplete the on-board battery or fuel. To develop the missing capability, the authors propose a low-complexity hyperspectral image classifier that approaches the performance of prevalent classifiers. This research determines that the new method will require at least 19-fold less computing capacity than the prevalent classifier. To assess relative performances, the authors developed a benchmark that compares a statistic of library endmember separability in their respective feature spaces.

  10. Low-Rank Sparse Coding for Image Classification

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Xu, Changsheng; Ahuja, Narendra

    2013-01-01

    In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

  11. Low-Rank Sparse Coding for Image Classification

    KAUST Repository

    Zhang, Tianzhu

    2013-12-01

    In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

  12. Research on Remote Sensing Image Classification Based on Feature Level Fusion

    Science.gov (United States)

    Yuan, L.; Zhu, G.

    2018-04-01

    Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

  13. Supervised Convolutional Sparse Coding

    KAUST Repository

    Affara, Lama Ahmed

    2018-04-08

    Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

  14. A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

    Directory of Open Access Journals (Sweden)

    Flavia Di Palma

    2016-07-01

    Full Text Available The negative impacts of land take on natural components and economic resources affect planning choices and territorial policies. The importance of land take monitoring, in Italy, has been only recently considered, but despite this awareness, in the great part of the country, effective monitoring and containment measures have not been started, yet. This research proposes a methodology to map and monitor land use changes. To this end, a time series from 1985–2010, based on the multi-temporal Landsat data Thematic Mapper (TM, has been analyzed in the Vulture Alto-Bradano area, a mountain zone of the Basilicata region (Southern Italy. Results confirm a double potentiality of using these data: on the one hand, the use of multi-temporal Landsat data allows going very back in time, producing accurate datasets that provide a phenomenon trend over time; on the other hand, these data can be considered a first experience of open data in the field of spatial information. The proposed methodology provides agencies, local authorities and practitioners with a valuable tool to implement monitoring actions. This represents the first step to pursue territorial governance methods based on sustainability, limiting the land take.

  15. Semi-automatic supervised classification of minerals from x-ray mapping images

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Flesche, Harald; Larsen, Rasmus

    1998-01-01

    to a small area in order to allow for the estimation of a variance-covariance matrix. This expansion is controlled by upper limits for the spatial and Euclidean spectral distances from the seed point. Second, after this initial expansion the growing of the training set is controlled by an upper limit...... is obtained by excluding observations that have high Mahalanobis distances to the training class mean. Spatial closeness is obtained by requiring connectivity. The marginal effects of changes in the parameters that are input to the seed growing algorithm are evaluated. Initially, the seed is expanded...... for the Mahalanobis distance to the current estimate of the class centre. Also, the estimates of class centres and covariance matrices may be continuously updated or the initial estimates may be used. Finally, the effect of the operator's choice of seed among a number of potential seeding points is evaluated. After...

  16. Classification of quantitative light-induced fluorescence images using convolutional neural network

    NARCIS (Netherlands)

    Imangaliyev, S.; van der Veen, M.H.; Volgenant, C.M.C.; Loos, B.G.; Keijser, B.J.F.; Crielaard, W.; Levin, E.; Lintas, A.; Rovetta, S.; Verschure, P.F.M.J.; Villa, A.E.P.

    2017-01-01

    Images are an important data source for diagnosis of oral diseases. The manual classification of images may lead to suboptimal treatment procedures due to subjective errors. In this paper an image classification algorithm based on Deep Learning framework is applied to Quantitative Light-induced

  17. Image segmentation and particles classification using texture analysis method

    Directory of Open Access Journals (Sweden)

    Mayar Aly Atteya

    Full Text Available Introduction: Ingredients of oily fish include a large amount of polyunsaturated fatty acids, which are important elements in various metabolic processes of humans, and have also been used to prevent diseases. However, in an attempt to reduce cost, recent developments are starting a replace the ingredients of fish oil with products of microalgae, that also produce polyunsaturated fatty acids. To do so, it is important to closely monitor morphological changes in algae cells and monitor their age in order to achieve the best results. This paper aims to describe an advanced vision-based system to automatically detect, classify, and track the organic cells using a recently developed SOPAT-System (Smart On-line Particle Analysis Technology, a photo-optical image acquisition device combined with innovative image analysis software. Methods The proposed method includes image de-noising, binarization and Enhancement, as well as object recognition, localization and classification based on the analysis of particles’ size and texture. Results The methods allowed for correctly computing cell’s size for each particle separately. By computing an area histogram for the input images (1h, 18h, and 42h, the variation could be observed showing a clear increase in cell. Conclusion The proposed method allows for algae particles to be correctly identified with accuracies up to 99% and classified correctly with accuracies up to 100%.

  18. Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging

    Science.gov (United States)

    Schwedt, Todd J.; Chong, Catherine D.; Wu, Teresa; Gaw, Nathan; Fu, Yinlin; Li, Jing

    2015-01-01

    Background The International Classification of Headache Disorders provides criteria for the diagnosis and subclassification of migraine. Since there is no objective gold standard by which to test these diagnostic criteria, the criteria are based on the consensus opinion of content experts. Accurate migraine classifiers consisting of brain structural measures could serve as an objective gold standard by which to test and revise diagnostic criteria. The objectives of this study were to utilize magnetic resonance imaging measures of brain structure for constructing classifiers: 1) that accurately identify individuals as having chronic vs. episodic migraine vs. being a healthy control; and 2) that test the currently used threshold of 15 headache days/month for differentiating chronic migraine from episodic migraine. Methods Study participants underwent magnetic resonance imaging for determination of regional cortical thickness, cortical surface area, and volume. Principal components analysis combined structural measurements into principal components accounting for 85% of variability in brain structure. Models consisting of these principal components were developed to achieve the classification objectives. Ten-fold cross validation assessed classification accuracy within each of the ten runs, with data from 90% of participants randomly selected for classifier development and data from the remaining 10% of participants used to test classification performance. Headache frequency thresholds ranging from 5–15 headache days/month were evaluated to determine the threshold allowing for the most accurate subclassification of individuals into lower and higher frequency subgroups. Results Participants were 66 migraineurs and 54 healthy controls, 75.8% female, with an average age of 36 +/− 11 years. Average classifier accuracies were: a) 68% for migraine (episodic + chronic) vs. healthy controls; b) 67.2% for episodic migraine vs. healthy controls; c) 86.3% for chronic

  19. Automated processing of webcam images for phenological classification.

    Directory of Open Access Journals (Sweden)

    Ludwig Bothmann

    Full Text Available Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels' time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the

  20. Exoplanet Classification and Yield Estimates for Direct Imaging Missions

    Science.gov (United States)

    Kopparapu, Ravi Kumar; Hébrard, Eric; Belikov, Rus; Batalha, Natalie M.; Mulders, Gijs D.; Stark, Chris; Teal, Dillon; Domagal-Goldman, Shawn; Mandell, Avi

    2018-04-01

    Future NASA concept missions that are currently under study, like the Habitable Exoplanet Imaging Mission (HabEx) and the Large Ultra-violet Optical Infra Red Surveyor, could discover a large diversity of exoplanets. We propose here a classification scheme that distinguishes exoplanets into different categories based on their size and incident stellar flux, for the purpose of providing the expected number of exoplanets observed (yield) with direct imaging missions. The boundaries of this classification can be computed using the known chemical behavior of gases and condensates at different pressures and temperatures in a planetary atmosphere. In this study, we initially focus on condensation curves for sphalerite ZnS, {{{H}}}2{{O}}, {CO}}2, and {CH}}4. The order in which these species condense in a planetary atmosphere define the boundaries between different classes of planets. Broadly, the planets are divided into rocky planets (0.5–1.0 R ⊕), super-Earths (1.0–1.75 R ⊕), sub-Neptunes (1.75–3.5 R ⊕), sub-Jovians (3.5–6.0 R ⊕), and Jovians (6–14.3 R ⊕) based on their planet sizes, and “hot,” “warm,” and “cold” based on the incident stellar flux. We then calculate planet occurrence rates within these boundaries for different kinds of exoplanets, η planet, using the community coordinated results of NASA’s Exoplanet Program Analysis Group’s Science Analysis Group-13 (SAG-13). These occurrence rate estimates are in turn used to estimate the expected exoplanet yields for direct imaging missions of different telescope diameters.

  1. Radiation dose reduction in digital breast tomosynthesis (DBT) by means of deep-learning-based supervised image processing

    Science.gov (United States)

    Liu, Junchi; Zarshenas, Amin; Qadir, Ammar; Wei, Zheng; Yang, Limin; Fajardo, Laurie; Suzuki, Kenji

    2018-03-01

    To reduce cumulative radiation exposure and lifetime risks for radiation-induced cancer from breast cancer screening, we developed a deep-learning-based supervised image-processing technique called neural network convolution (NNC) for radiation dose reduction in DBT. NNC employed patched-based neural network regression in a convolutional manner to convert lower-dose (LD) to higher-dose (HD) tomosynthesis images. We trained our NNC with quarter-dose (25% of the standard dose: 12 mAs at 32 kVp) raw projection images and corresponding "teaching" higher-dose (HD) images (200% of the standard dose: 99 mAs at 32 kVp) of a breast cadaver phantom acquired with a DBT system (Selenia Dimensions, Hologic, CA). Once trained, NNC no longer requires HD images. It converts new LD images to images that look like HD images; thus the term "virtual" HD (VHD) images. We reconstructed tomosynthesis slices on a research DBT system. To determine a dose reduction rate, we acquired 4 studies of another test phantom at 4 different radiation doses (1.35, 2.7, 4.04, and 5.39 mGy entrance dose). Structural SIMilarity (SSIM) index was used to evaluate the image quality. For testing, we collected half-dose (50% of the standard dose: 32+/-14 mAs at 33+/-5 kVp) and full-dose (standard dose: 68+/-23 mAs at 33+/-5 kvp) images of 10 clinical cases with the DBT system at University of Iowa Hospitals and Clinics. NNC converted half-dose DBT images of 10 clinical cases to VHD DBT images that were equivalent to full dose DBT images. Our cadaver phantom experiment demonstrated 79% dose reduction.

  2. A contextual image segmentation system using a priori information for automatic data classification in nuclear physics

    International Nuclear Information System (INIS)

    Benkirane, A.; Auger, G.; Chbihi, A.; Bloyet, D.; Plagnol, E.

    1994-01-01

    This paper presents an original approach to solve an automatic data classification problem by means of image processing techniques. The classification is achieved using image segmentation techniques for extracting the meaningful classes. Two types of information are merged for this purpose: the information contained in experimental images and a priori information derived from underlying physics (and adapted to image segmentation problem). This data fusion is widely used at different stages of the segmentation process. This approach yields interesting results in terms of segmentation performances, even in very noisy cases. Satisfactory classification results are obtained in cases where more ''classical'' automatic data classification methods fail. (authors). 25 refs., 14 figs., 1 append

  3. A contextual image segmentation system using a priori information for automatic data classification in nuclear physics

    Energy Technology Data Exchange (ETDEWEB)

    Benkirane, A; Auger, G; Chbihi, A [Grand Accelerateur National d` Ions Lourds (GANIL), 14 - Caen (France); Bloyet, D [Caen Univ., 14 (France); Plagnol, E [Paris-11 Univ., 91 - Orsay (France). Inst. de Physique Nucleaire

    1994-12-31

    This paper presents an original approach to solve an automatic data classification problem by means of image processing techniques. The classification is achieved using image segmentation techniques for extracting the meaningful classes. Two types of information are merged for this purpose: the information contained in experimental images and a priori information derived from underlying physics (and adapted to image segmentation problem). This data fusion is widely used at different stages of the segmentation process. This approach yields interesting results in terms of segmentation performances, even in very noisy cases. Satisfactory classification results are obtained in cases where more ``classical`` automatic data classification methods fail. (authors). 25 refs., 14 figs., 1 append.

  4. Classification and printability of EUV mask defects from SEM images

    Science.gov (United States)

    Cho, Wonil; Price, Daniel; Morgan, Paul A.; Rost, Daniel; Satake, Masaki; Tolani, Vikram L.

    2017-10-01

    Classification and Printability of EUV Mask Defects from SEM images EUV lithography is starting to show more promise for patterning some critical layers at 5nm technology node and beyond. However, there still are many key technical obstacles to overcome before bringing EUV Lithography into high volume manufacturing (HVM). One of the greatest obstacles is manufacturing defect-free masks. For pattern defect inspections in the mask-shop, cutting-edge 193nm optical inspection tools have been used so far due to lacking any e-beam mask inspection (EBMI) or EUV actinic pattern inspection (API) tools. The main issue with current 193nm inspection tools is the limited resolution for mask dimensions targeted for EUV patterning. The theoretical resolution limit for 193nm mask inspection tools is about 60nm HP on masks, which means that main feature sizes on EUV masks will be well beyond the practical resolution of 193nm inspection tools. Nevertheless, 193nm inspection tools with various illumination conditions that maximize defect sensitivity and/or main-pattern modulation are being explored for initial EUV defect detection. Due to the generally low signal-to-noise in the 193nm inspection imaging at EUV patterning dimensions, these inspections often result in hundreds and thousands of defects which then need to be accurately reviewed and dispositioned. Manually reviewing each defect is difficult due to poor resolution. In addition, the lack of a reliable aerial dispositioning system makes it very challenging to disposition for printability. In this paper, we present the use of SEM images of EUV masks for higher resolution review and disposition of defects. In this approach, most of the defects detected by the 193nm inspection tools are first imaged on a mask SEM tool. These images together with the corresponding post-OPC design clips are provided to KLA-Tencor's Reticle Decision Center (RDC) platform which provides ADC (Automated Defect Classification) and S2A (SEM

  5. [Classification and MR imaging of triangular fibrocartilage complex lesions].

    Science.gov (United States)

    Zhan, H L; Liu, Y; Bai, R J; Qian, Z H; Ye, W; Li, Y X; Wu, B D

    2016-06-07

    To explore the MRI characteristics of injuries of triangular fibrocartilage complex (TFCC), and provide imaging basis for the early diagnosis and treatment of the injuries. A total of 10 healthy volunteers without wrist injuries and 200 patients from Beijing Jishuitan Hospital who complained ulnar-sided wrist pain and were highly suspected as the injury of TFCC underwent the wrist magnetic resonance examination. All subjects were in a prone position and underwent examination on coronal T1WI scan and PD-FS on 3 planes respectively. Then the MRI characteristics of 3 healthy volunteers and 67 patients with TFCC injuries that confirmed by operation were analyzed. According to the comparative analysis of normal anatomy and Palmer classification, the injuries were classified and MRI features of different types of injuries were analyzed. At last, imaging findings were compared with surgical results. Three healthy volunteers without injuries showed mainly in low signal intensity on T1WI and PD-FS images. According to Palmer classification, there were 52 traumatic injuries (ⅠA 9, ⅠB 25, ⅠC 3, ⅠD 13, In addition, 1 has central perforation and ulnar avulsion and 1 has ulnar and radial injuries simultaneously) and 15 degenerative injuries (ⅡA 5, ⅡB 1, ⅡC 2 , ⅡD 1 , ⅡE 6) among 67 patients. The central perforation mainly demonstrated as linear high signal perpendicular to the disk, and run in a sagittal line. The ulnar, distal, and radial avulsion mainly showed the injuries were irregular, the structures were ambiguous, and there was high signal intensity in the injured structures on PD-FS. Degenerative injuries demonstrated the irregularity of TFC and heterogeneous signals on PD-FS. There were mixed intermediate-high signals and changes in the articular cartilage of lunate and ulna, high signal in the lunotriquetral ligament and ulnocarpal or radioulnar arthritis. MRI can demonstrate the anatomy of TFCC accurately, evaluate and make the general classification

  6. Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN and Landsat Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Karsten Schulz

    2009-11-01

    Full Text Available Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML or k-Nearest Neighbor (k-NN indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.

  7. Classification of CT brain images based on deep learning networks.

    Science.gov (United States)

    Gao, Xiaohong W; Hui, Rui; Tian, Zengmin

    2017-01-01

    While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3-5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information

  8. Advanced Land Use Classification for Nigeriasat-1 Image of Lake Chad Basin

    Science.gov (United States)

    Babamaaji, R.; Park, C.; Lee, J.

    2009-12-01

    Lake Chad is a shrinking freshwater lake that has been significantly reduced to about 1/20 of its original size in the 1960’s. The severe draughts in 1970’s and 1980’s and following overexploitations of water resulted in the shortage of surface water in the lake and the surrounding rivers. Ground water resources are in scarcity too as ground water recharge is mostly made by soil infiltration through soil and land cover, but this surface cover is now experiencing siltation and expansion of wetland with invasive species. Large changes in land use and water management practices have taken place in the last 50 years including: removal of water from river systems for irrigation and consumption, degradation of forage land by overgrazing, deforestation, replacing natural ecosystems with mono-cultures, and construction of dams. Therefore, understanding the change of land use and its characteristics must be a first step to find how such changes disturb the water cycle around the lake and affect the shrinkage of the lake. Before any useful thematic information can be extracted from remote sensing data, a land cover classification system has to be developed to obtain the classes of interest. A combination of classification systems used by Global land cover, Water Resources eAtlass and Lake Chad Basin Commission gave rise to 7 land cover classes comprising of - Cropland, vegetation, grassland, water body, shrub-land, farmland ( mostly irrigated) and bareland (i.e. clear land). Supervised Maximum likelihood classification method was used with 15 reference points per class chosen. At the end of the classification, the overall accuracy is 93.33%. Producer’s accuracy for vegetation is 40% compare to the user’s accuracy that is 66.67 %. The reason is that the vegetation is similar to shrub land, it is very hard to differentiate between the vegetation and other plants, and therefore, most of the vegetation is classified as shrub land. Most of the waterbodies are occupied

  9. Cloud classification using whole-sky imager data

    Energy Technology Data Exchange (ETDEWEB)

    Buch, K.A. Jr.; Sun, C.H.; Thorne, L.R. [Sandia National Labs., Livermore, CA (United States)

    1996-04-01

    Clouds are one of the most important moderators of the earth radiation budget and one of the least understood. The effect that clouds have on the reflection and absorption of solar and terrestrial radiation is strongly influenced by their shape, size, and composition. Physically accurate parameterization of clouds is necessary for any general circulation model (GCM) to yield meaningful results. The work presented here is part of a larger project that is aimed at producing realistic three-dimensional (3D) volume renderings of cloud scenes based on measured data from real cloud scenes. These renderings will provide the important shape information for parameterizing GCMs. The specific goal of the current study is to develop an algorithm that automatically classifies (by cloud type) the clouds observed in the scene. This information will assist the volume rendering program in determining the shape of the cloud. Much work has been done on cloud classification using multispectral satellite images. Most of these references use some kind of texture measure to distinguish the different cloud types and some also use topological features (such as cloud/sky connectivity or total number of clouds). A wide variety of classification methods has been used, including neural networks, various types of clustering, and thresholding. The work presented here uses binary decision trees to distinguish the different cloud types based on cloud features vectors.

  10. A neural network approach for radiographic image classification in NDT

    International Nuclear Information System (INIS)

    Lavayssiere, B.

    1993-05-01

    Radiography is used by EDF for pipe inspection in nuclear power plants in order to detect defects. The radiographs obtained are then digitized in a well-defined protocol. The aim of EDF consists of developing a non destructive testing system for recognizing defects. In this note, we describe the recognition procedure of areas with defects. We first present the digitization protocol, specifies the poor quality of images under study and propose a procedure to enhance defects. We then examine the problem raised by the choice of good features for classification. After having proved that statistical or standard textural features such as homogeneity, entropy or contrast are not relevant, we develop a geometrical-statistical approach based on the cooperation between signal correlations study and regional extrema analysis. The principle consists of analysing and comparing for areas with defects and without any defect, the evolution of conditional probabilities matrices for increasing neighbourhood sizes, the shape of variograms and the location of regional minima. We demonstrate that anisotropy and surface of series of 'comet tails' associated with probability matrices, variograms slope and statistical indices, regional extrema location, are features able to discriminate areas with defects from areas without any. The classification is then realized by a neural network, which structure, properties and learning mechanisms are detailed. Finally we discuss the results. (author). 5 figs., 21 refs

  11. Renal cell carcinoma: histological classification and correlation with imaging findings

    Energy Technology Data Exchange (ETDEWEB)

    Muglia, Valdair F., E-mail: fmuglia@fmrp.usp.br [Universidade de Sao Paulo (CCIFM/FMRP/USP), Ribeirao Preto, SP (Brazil). Centro de Ciencias das Imagens e Fisica Medica. Faculdade de Medicina; Prando, Adilson [Universidade Estadual de Campinas (UNICAMP), SP (Brazil); Hospital Vera Cruz, Campinas, SP (Brazil). Dept. de Imaginologia

    2015-05-15

    Renal cell carcinoma (RCC) is the seventh most common histological type of cancer in the Western world and has shown a sustained increase in its prevalence. The histological classification of RCCs is of utmost importance, considering the significant prognostic and therapeutic implications of its histological subtypes. Imaging methods play an outstanding role in the diagnosis, staging and follow-up of RCC. Clear cell, papillary and chromophobe are the most common histological subtypes of RCC, and their preoperative radiological characterization, either followed or not by confirmatory percutaneous biopsy, may be particularly useful in cases of poor surgical condition, metastatic disease, central mass in a solitary kidney, and in patients eligible for molecular targeted therapy. New strategies recently developed for treating renal cancer, such as cryo and radiofrequency ablation, molecularly targeted therapy and active surveillance also require appropriate preoperative characterization of renal masses. Less common histological types, although sharing nonspecific imaging features, may be suspected on the basis of clinical and epidemiological data. The present study is aimed at reviewing the main clinical and imaging findings of histological RCC subtypes. (author)

  12. Spatial encoding of visual words for image classification

    Science.gov (United States)

    Liu, Dong; Wang, Shengsheng; Porikli, Fatih

    2016-05-01

    Appearance-based bag-of-visual words (BoVW) models are employed to represent the frequency of a vocabulary of local features in an image. Due to their versatility, they are widely popular, although they ignore the underlying spatial context and relationships among the features. Here, we present a unified representation that enhances BoVWs with explicit local and global structure models. Three aspects of our method should be noted in comparison to the previous approaches. First, we use a local structure feature that encodes the spatial attributes between a pair of points in a discriminative fashion using class-label information. We introduce a bag-of-structural words (BoSW) model for the given image set and describe each image with this model on its coarsely sampled relevant keypoints. We then combine the codebook histograms of BoVW and BoSW to train a classifier. Rigorous experimental evaluations on four benchmark data sets demonstrate that the unified representation outperforms the conventional models and compares favorably to more sophisticated scene classification techniques.

  13. Incorporating Open Source Data for Bayesian Classification of Urban Land Use From VHR Stereo Images

    NARCIS (Netherlands)

    Li, Mengmeng; De Beurs, Kirsten M.; Stein, Alfred; Bijker, Wietske

    2017-01-01

    This study investigates the incorporation of open source data into a Bayesian classification of urban land use from very high resolution (VHR) stereo satellite images. The adopted classification framework starts from urban land cover classification, proceeds to building-type characterization, and

  14. Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

    Directory of Open Access Journals (Sweden)

    Abdullah-Al Nahid

    2017-01-01

    Full Text Available Breast cancer is one of the largest causes of women’s death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors’ and physicians’ time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN, Logic Based classifiers such as the Random Forest (RF algorithm, Support Vector Machines (SVM, Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

  15. Involvement of Machine Learning for Breast Cancer Image Classification: A Survey.

    Science.gov (United States)

    Nahid, Abdullah-Al; Kong, Yinan

    2017-01-01

    Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

  16. MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH

    Data.gov (United States)

    National Aeronautics and Space Administration — MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH VARUN CHANDOLA AND RANGA RAJU VATSAVAI Abstract. Multispectral remote sensing images have...

  17. IMPACTS OF PATCH SIZE AND LANDSCAPE HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    Science.gov (United States)

    Impacts of Patch Size and Landscape Heterogeneity on Thematic Image Classification Accuracy. Currently, most thematic accuracy assessments of classified remotely sensed images oily account for errors between the various classes employed, at particular pixels of interest, thu...

  18. APPLICATION OF FUSION WITH SAR AND OPTICAL IMAGES IN LAND USE CLASSIFICATION BASED ON SVM

    Directory of Open Access Journals (Sweden)

    C. Bao

    2012-07-01

    Full Text Available As the increment of remote sensing data with multi-space resolution, multi-spectral resolution and multi-source, data fusion technologies have been widely used in geological fields. Synthetic Aperture Radar (SAR and optical camera are two most common sensors presently. The multi-spectral optical images express spectral features of ground objects, while SAR images express backscatter information. Accuracy of the image classification could be effectively improved fusing the two kinds of images. In this paper, Terra SAR-X images and ALOS multi-spectral images were fused for land use classification. After preprocess such as geometric rectification, radiometric rectification noise suppression and so on, the two kind images were fused, and then SVM model identification method was used for land use classification. Two different fusion methods were used, one is joining SAR image into multi-spectral images as one band, and the other is direct fusing the two kind images. The former one can raise the resolution and reserve the texture information, and the latter can reserve spectral feature information and improve capability of identifying different features. The experiment results showed that accuracy of classification using fused images is better than only using multi-spectral images. Accuracy of classification about roads, habitation and water bodies was significantly improved. Compared to traditional classification method, the method of this paper for fused images with SVM classifier could achieve better results in identifying complicated land use classes, especially for small pieces ground features.

  19. Sparse Detector Imaging Sensor with Two-Class Silhouette Classification

    Directory of Open Access Journals (Sweden)

    David Russomanno

    2008-12-01

    Full Text Available This paper presents the design and test of a simple active near-infrared sparse detector imaging sensor. The prototype of the sensor is novel in that it can capture remarkable silhouettes or profiles of a wide-variety of moving objects, including humans, animals, and vehicles using a sparse detector array comprised of only sixteen sensing elements deployed in a vertical configuration. The prototype sensor was built to collect silhouettes for a variety of objects and to evaluate several algorithms for classifying the data obtained from the sensor into two classes: human versus non-human. Initial tests show that the classification of individually sensed objects into two classes can be achieved with accuracy greater than ninety-nine percent (99% with a subset of the sixteen detectors using a representative dataset consisting of 512 signatures. The prototype also includes a Webservice interface such that the sensor can be tasked in a network-centric environment. The sensor appears to be a low-cost alternative to traditional, high-resolution focal plane array imaging sensors for some applications. After a power optimization study, appropriate packaging, and testing with more extensive datasets, the sensor may be a good candidate for deployment in vast geographic regions for a myriad of intelligent electronic fence and persistent surveillance applications, including perimeter security scenarios.

  20. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

    Science.gov (United States)

    McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

    2018-04-01

    Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Dose classification scheme for digital imaging techniques in diagnostic radiology

    International Nuclear Information System (INIS)

    Hojreh, A.

    2002-04-01

    Purpose: image quality in diagnostic radiology is determined in crucial extent by the signal-noise-ratio, which is proportional to the applied x-ray dose. Onward technological developments in the diagnostic radiology are therefore frequently connected with a dose increase, which subjectively is hardly or even not perceptible. The aim of this work was to define reproducible standards for image quality as a function of dose and expected therapeutical consequence in case of computed tomography of the paranasal sinuses and the upper and lower jaw (dental CT), whereby practical-clinical purposes are considered. Materials and methods: the image quality of computed tomography of the paranasal sinuses and dental CT was determined by standard deviation of the CT-numbers (pixel noise) in a region of interest of the phantom of American Association of Physicists in Medicine (AAPM phantom) and additionally in the patients CT images. The diagnostic quality of the examination was classified on the basis of patients CT images in three dose levels (low dose, standard dose and high dose). Results: the pixel noise of CT of the paranasal sinuses with soft tissue reconstruction amounts to 19.3 Hounsfield units (HU) for low dose, 8.8 HU for standard dose, and below 8 HU for high dose. The pixel noise of the dental CT with bone (high resolution) reconstruction amounts to 344 HU for low dose, 221 HU for standard dose, and below 200 HU for high dose. Suitable indications for low dose CT are the scanning of body regions with high contrast differences, like the bony delimitations of air-filled spaces of the facial bones, and radiological follow-up examinations with dedicated questions such as axis determination in dental implantology, as well as the images of objects with small diameter such as in case of children. The standard dose CT can be recommended for all cases, in which precise staging of the illness plays an indispensable role for the diagnosis and therapy planning. With high dose

  2. Image processing pipeline for segmentation and material classification based on multispectral high dynamic range polarimetric images.

    Science.gov (United States)

    Martínez-Domingo, Miguel Ángel; Valero, Eva M; Hernández-Andrés, Javier; Tominaga, Shoji; Horiuchi, Takahiko; Hirai, Keita

    2017-11-27

    We propose a method for the capture of high dynamic range (HDR), multispectral (MS), polarimetric (Pol) images of indoor scenes using a liquid crystal tunable filter (LCTF). We have included the adaptive exposure estimation (AEE) method to fully automatize the capturing process. We also propose a pre-processing method which can be applied for the registration of HDR images after they are already built as the result of combining different low dynamic range (LDR) images. This method is applied to ensure a correct alignment of the different polarization HDR images for each spectral band. We have focused our efforts in two main applications: object segmentation and classification into metal and dielectric classes. We have simplified the segmentation using mean shift combined with cluster averaging and region merging techniques. We compare the performance of our segmentation with that of Ncut and Watershed methods. For the classification task, we propose to use information not only in the highlight regions but also in their surrounding area, extracted from the degree of linear polarization (DoLP) maps. We present experimental results which proof that the proposed image processing pipeline outperforms previous techniques developed specifically for MSHDRPol image cubes.

  3. Parsimonious classification of binary lacunarity data computed from food surface images using kernel principal component analysis and artificial neural networks.

    Science.gov (United States)

    Iqbal, Abdullah; Valous, Nektarios A; Sun, Da-Wen; Allen, Paul

    2011-02-01

    Lacunarity is about quantifying the degree of spatial heterogeneity in the visual texture of imagery through the identification of the relationships between patterns and their spatial configurations in a two-dimensional setting. The computed lacunarity data can designate a mathematical index of spatial heterogeneity, therefore the corresponding feature vectors should possess the necessary inter-class statistical properties that would enable them to be used for pattern recognition purposes. The objectives of this study is to construct a supervised parsimonious classification model of binary lacunarity data-computed by Valous et al. (2009)-from pork ham slice surface images, with the aid of kernel principal component analysis (KPCA) and artificial neural networks (ANNs), using a portion of informative salient features. At first, the dimension of the initial space (510 features) was reduced by 90% in order to avoid any noise effects in the subsequent classification. Then, using KPCA, the first nineteen kernel principal components (99.04% of total variance) were extracted from the reduced feature space, and were used as input in the ANN. An adaptive feedforward multilayer perceptron (MLP) classifier was employed to obtain a suitable mapping from the input dataset. The correct classification percentages for the training, test and validation sets were 86.7%, 86.7%, and 85.0%, respectively. The results confirm that the classification performance was satisfactory. The binary lacunarity spatial metric captured relevant information that provided a good level of differentiation among pork ham slice images. Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.

  4. Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

    KAUST Repository

    Zhu, Xiaofeng

    2015-05-28

    This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

  5. Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.

    Science.gov (United States)

    Dolz, Jose; Laprie, Anne; Ken, Soléakhéna; Leroy, Henri-Arthur; Reyns, Nicolas; Massoptier, Laurent; Vermandel, Maximilien

    2016-01-01

    To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm(3), where the value for best performing IIVs configuration was 0.85 cm(3), representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

  6. Sensitivity study of a semiautomatic supervised classifier applied to minerals from x-ray mapping images

    DEFF Research Database (Denmark)

    Larsen, Rasmus; Nielsen, Allan Aasbjerg; Flesche, Harald

    2000-01-01

    This paper addresses the problem of assessing the robustness with respect to change in parameters of an integrated training and classification routine for minerals commonly encountered in siliciclastic or carbonate rocks. Twelve chemical elements are mapped from thin sections by energy dispersive...

  7. Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis

    Science.gov (United States)

    Itoh, Hayato; Mori, Yuichi; Misawa, Masashi; Oda, Masahiro; Kudo, Shin-ei; Mori, Kensaku

    2018-02-01

    This paper presents a new classification method for endocytoscopic images. Endocytoscopy is a new endoscope that enables us to perform conventional endoscopic observation and ultramagnified observation of cell level. This ultramagnified views (endocytoscopic images) make possible to perform pathological diagnosis only on endo-scopic views of polyps during colonoscopy. However, endocytoscopic image diagnosis requires higher experiences for physicians. An automated pathological diagnosis system is required to prevent the overlooking of neoplastic lesions in endocytoscopy. For this purpose, we propose a new automated endocytoscopic image classification method that classifies neoplastic and non-neoplastic endocytoscopic images. This method consists of two classification steps. At the first step, we classify an input image by support vector machine. We forward the image to the second step if the confidence of the first classification is low. At the second step, we classify the forwarded image by convolutional neural network. We reject the input image if the confidence of the second classification is also low. We experimentally evaluate the classification performance of the proposed method. In this experiment, we use about 16,000 and 4,000 colorectal endocytoscopic images as training and test data, respectively. The results show that the proposed method achieves high sensitivity 93.4% with small rejection rate 9.3% even for difficult test data.

  8. Performance Evaluation of Frequency Transform Based Block Classification of Compound Image Segmentation Techniques

    Science.gov (United States)

    Selwyn, Ebenezer Juliet; Florinabel, D. Jemi

    2018-04-01

    Compound image segmentation plays a vital role in the compression of computer screen images. Computer screen images are images which are mixed with textual, graphical, or pictorial contents. In this paper, we present a comparison of two transform based block classification of compound images based on metrics like speed of classification, precision and recall rate. Block based classification approaches normally divide the compound images into fixed size blocks of non-overlapping in nature. Then frequency transform like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are applied over each block. Mean and standard deviation are computed for each 8 × 8 block and are used as features set to classify the compound images into text/graphics and picture/background block. The classification accuracy of block classification based segmentation techniques are measured by evaluation metrics like precision and recall rate. Compound images of smooth background and complex background images containing text of varying size, colour and orientation are considered for testing. Experimental evidence shows that the DWT based segmentation provides significant improvement in recall rate and precision rate approximately 2.3% than DCT based segmentation with an increase in block classification time for both smooth and complex background images.

  9. Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Chen Xing

    2016-01-01

    Full Text Available Deep learning methods have been successfully applied to learn feature representations for high-dimensional data, where the learned features are able to reveal the nonlinear properties exhibited in the data. In this paper, deep learning method is exploited for feature extraction of hyperspectral data, and the extracted features can provide good discriminability for classification task. Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning. We utilized stacked denoise autoencoder (SDAE method to pretrain the network, which is robust to noise. In the top layer of the network, logistic regression (LR approach is utilized to perform supervised fine-tuning and classification. Since sparsity of features might improve the separation capability, we utilized rectified linear unit (ReLU as activation function in SDAE to extract high level and sparse features. Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR can achieve higher accuracies than the popular support vector machine (SVM classifier.

  10. Fast Low-Rank Shared Dictionary Learning for Image Classification.

    Science.gov (United States)

    Tiep Huu Vu; Monga, Vishal

    2017-11-01

    Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e., claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Furthermore, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image data sets establish the advantages of our method over the state-of-the-art dictionary learning methods.

  11. Automated color classification of urine dipstick image in urine examination

    Science.gov (United States)

    Rahmat, R. F.; Royananda; Muchtar, M. A.; Taqiuddin, R.; Adnan, S.; Anugrahwaty, R.; Budiarto, R.

    2018-03-01

    Urine examination using urine dipstick has long been used to determine the health status of a person. The economical and convenient use of urine dipstick is one of the reasons urine dipstick is still used to check people health status. The real-life implementation of urine dipstick is done manually, in general, that is by comparing it with the reference color visually. This resulted perception differences in the color reading of the examination results. In this research, authors used a scanner to obtain the urine dipstick color image. The use of scanner can be one of the solutions in reading the result of urine dipstick because the light produced is consistent. A method is required to overcome the problems of urine dipstick color matching and the test reference color that have been conducted manually. The method proposed by authors is Euclidean Distance, Otsu along with RGB color feature extraction method to match the colors on the urine dipstick with the standard reference color of urine examination. The result shows that the proposed approach was able to classify the colors on a urine dipstick with an accuracy of 95.45%. The accuracy of color classification on urine dipstick against the standard reference color is influenced by the level of scanner resolution used, the higher the scanner resolution level, the higher the accuracy.

  12. Classification

    Science.gov (United States)

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  13. Minimisation de fonctions de perte calibrée pour la classification des images

    OpenAIRE

    Bel Haj Ali , Wafa

    2013-01-01

    Image classification becomes a big challenge since it concerns on the one hand millions or billions of images that are available on the web and on the other hand images used for critical real-time applications. This classification involves in general learning methods and classifiers that must require both precision as well as speed performance. These learning problems concern a large number of application areas: namely, web applications (profiling, targeting, social networks, search engines),...

  14. Multi Angle Imaging With Spectral Remote Sensing for Scene Classification

    National Research Council Canada - National Science Library

    Prasert, Sunyaruk

    2005-01-01

    .... This study analyses the BRDF (Bidirectional Reflectance Distribution Function) impact and effectiveness of texture analysis on terrain classification within Fresno County area in state of California...

  15. Land use/cover classification in the Brazilian Amazon using satellite images.

    Science.gov (United States)

    Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

    2012-09-01

    Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

  16. Biomedical imaging modality classification using combined visual features and textual terms.

    Science.gov (United States)

    Han, Xian-Hua; Chen, Yen-Wei

    2011-01-01

    We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

  17. Segmentation of Clinical Endoscopic Images Based on the Classification of Topological Vector Features

    Directory of Open Access Journals (Sweden)

    O. A. Dunaeva

    2013-01-01

    Full Text Available In this work, we describe a prototype of an automatic segmentation system and annotation of endoscopy images. The used algorithm is based on the classification of vectors of the topological features of the original image. We use the image processing scheme which includes image preprocessing, calculation of vector descriptors defined for every point of the source image and the subsequent classification of descriptors. Image preprocessing includes finding and selecting artifacts and equalizating the image brightness. In this work, we give the detailed algorithm of the construction of topological descriptors and the classifier creating procedure based on mutual sharing the AdaBoost scheme and a naive Bayes classifier. In the final section, we show the results of the classification of real endoscopic images.

  18. Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering.

    Science.gov (United States)

    Hu, Weiming; Hu, Ruiguang; Xie, Nianhua; Ling, Haibin; Maybank, Stephen

    2014-04-01

    In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scale space in general preserves or even enhances semantically important structures such as edges, lines, or flow-like structures in the foreground, and inhibits and smoothes clutter in the background. The image is classified using multiscale information fusion based on the original image, the image at the final scale at which the diffusion process converges, and the image at a midscale. Our algorithm emphasizes the foreground features, which are important for image classification. The background image regions, whether considered as contexts of the foreground or noise to the foreground, can be globally handled by fusing information from different scales. Experimental tests of the effectiveness of the multiscale space for the image classification are conducted on the following publicly available datasets: 1) the PASCAL 2005 dataset; 2) the Oxford 102 flowers dataset; and 3) the Oxford 17 flowers dataset, with high classification rates.

  19. Improvement of User's Accuracy Through Classification of Principal Component Images and Stacked Temporal Images

    Institute of Scientific and Technical Information of China (English)

    Nilanchal Patel; Brijesh Kumar Kaushal

    2010-01-01

    The classification accuracy of the various categories on the classified remotely sensed images are usually evaluated by two different measures of accuracy, namely, producer's accuracy (PA) and user's accuracy (UA). The PA of a category indicates to what extent the reference pixels of the category are correctly classified, whereas the UA ora category represents to what extent the other categories are less misclassified into the category in question. Therefore, the UA of the various categories determines the reliability of their interpretation on the classified image and is more important to the analyst than the PA. The present investigation has been performed in order to determine ifthere occurs improvement in the UA of the various categories on the classified image of the principal components of the original bands and on the classified image of the stacked image of two different years. We performed the analyses using the IRS LISS Ⅲ images of two different years, i.e., 1996 and 2009, that represent the different magnitude of urbanization and the stacked image of these two years pertaining to Ranchi area, Jharkhand, India, with a view to assessing the impacts of urbanization on the UA of the different categories. The results of the investigation demonstrated that there occurs significant improvement in the UA of the impervious categories in the classified image of the stacked image, which is attributable to the aggregation of the spectral information from twice the number of bands from two different years. On the other hand, the classified image of the principal components did not show any improvement in the UA as compared to the original images.

  20. Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

    Directory of Open Access Journals (Sweden)

    Jian Zhang

    2017-10-01

    Full Text Available Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR camera, for crop identification and leaf area index (LAI estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation.

  1. SUPERVISED AUTOMATIC HISTOGRAM CLUSTERING AND WATERSHED SEGMENTATION. APPLICATION TO MICROSCOPIC MEDICAL COLOR IMAGES

    Directory of Open Access Journals (Sweden)

    Olivier Lezoray

    2011-05-01

    Full Text Available In this paper, an approach to the segmentation of microscopic color images is addressed, and applied to medical images. The approach combines a clustering method and a region growing method. Each color plane is segmented independently relying on a watershed based clustering of the plane histogram. The marginal segmentation maps intersect in a label concordance map. The latter map is simplified based on the assumption that the color planes are correlated. This produces a simplified label concordance map containing labeled and unlabeled pixels. The formers are used as an image of seeds for a color watershed. This fast and robust segmentation scheme is applied to several types of medical images.

  2. Optimizing tree-species classification in hyperspectal images

    CSIR Research Space (South Africa)

    Barnard, E

    2010-11-01

    Full Text Available for classification. Scaling of these components so that all features have equal variance is found to be useful, and their best performance (88.9% accurate classification) is achieved with 15 scaled features and a support vector machine as classifier. A graphical...

  3. Classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2017-01-01

    This article presents and discusses definitions of the term “classification” and the related concepts “Concept/conceptualization,”“categorization,” “ordering,” “taxonomy” and “typology.” It further presents and discusses theories of classification including the influences of Aristotle...... and Wittgenstein. It presents different views on forming classes, including logical division, numerical taxonomy, historical classification, hermeneutical and pragmatic/critical views. Finally, issues related to artificial versus natural classification and taxonomic monism versus taxonomic pluralism are briefly...

  4. Supervised Learning

    Science.gov (United States)

    Rokach, Lior; Maimon, Oded

    This chapter summarizes the fundamental aspects of supervised methods. The chapter provides an overview of concepts from various interrelated fields used in subsequent chapters. It presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and challenges for data mining tasks.

  5. Automated otolith image classification with multiple views: an evaluation on Sciaenidae.

    Science.gov (United States)

    Wong, J Y; Chu, C; Chong, V C; Dhillon, S K; Loh, K H

    2016-08-01

    Combined multiple 2D views (proximal, anterior and ventral aspects) of the sagittal otolith are proposed here as a method to capture shape information for fish classification. Classification performance of single view compared with combined 2D views show improved classification accuracy of the latter, for nine species of Sciaenidae. The effects of shape description methods (shape indices, Procrustes analysis and elliptical Fourier analysis) on classification performance were evaluated. Procrustes analysis and elliptical Fourier analysis perform better than shape indices when single view is considered, but all perform equally well with combined views. A generic content-based image retrieval (CBIR) system that ranks dissimilarity (Procrustes distance) of otolith images was built to search query images without the need for detailed information of side (left or right), aspect (proximal or distal) and direction (positive or negative) of the otolith. Methods for the development of this automated classification system are discussed. © 2016 The Fisheries Society of the British Isles.

  6. A Spectral-Texture Kernel-Based Classification Method for Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2016-11-01

    Full Text Available Classification of hyperspectral images always suffers from high dimensionality and very limited labeled samples. Recently, the spectral-spatial classification has attracted considerable attention and can achieve higher classification accuracy and smoother classification maps. In this paper, a novel spectral-spatial classification method for hyperspectral images by using kernel methods is investigated. For a given hyperspectral image, the principle component analysis (PCA transform is first performed. Then, the first principle component of the input image is segmented into non-overlapping homogeneous regions by using the entropy rate superpixel (ERS algorithm. Next, the local spectral histogram model is applied to each homogeneous region to obtain the corresponding texture features. Because this step is performed within each homogenous region, instead of within a fixed-size image window, the obtained local texture features in the image are more accurate, which can effectively benefit the improvement of classification accuracy. In the following step, a contextual spectral-texture kernel is constructed by combining spectral information in the image and the extracted texture information using the linearity property of the kernel methods. Finally, the classification map is achieved by the support vector machines (SVM classifier using the proposed spectral-texture kernel. Experiments on two benchmark airborne hyperspectral datasets demonstrate that our method can effectively improve classification accuracies, even though only a very limited training sample is available. Specifically, our method can achieve from 8.26% to 15.1% higher in terms of overall accuracy than the traditional SVM classifier. The performance of our method was further compared to several state-of-the-art classification methods of hyperspectral images using objective quantitative measures and a visual qualitative evaluation.

  7. Deep supervised dictionary learning for no-reference image quality assessment

    Science.gov (United States)

    Huang, Yuge; Liu, Xuesong; Tian, Xiang; Zhou, Fan; Chen, Yaowu; Jiang, Rongxin

    2018-03-01

    We propose a deep convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA), i.e., accurate prediction of image quality without a reference image. The proposed model consists of three components such as a local feature extractor that is a fully CNN, an encoding module with an inherent dictionary that aggregates local features to output a fixed-length global quality-aware image representation, and a regression module that maps the representation to an image quality score. Our model can be trained in an end-to-end manner, and all of the parameters, including the weights of the convolutional layers, the dictionary, and the regression weights, are simultaneously learned from the loss function. In addition, the model can predict quality scores for input images of arbitrary sizes in a single step. We tested our method on commonly used image quality databases and showed that its performance is comparable with that of state-of-the-art general-purpose NR-IQA algorithms.

  8. Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias.

    Directory of Open Access Journals (Sweden)

    Carolina Reta

    Full Text Available Morphological identification of acute leukemia is a powerful tool used by hematologists to determine the family of such a disease. In some cases, experienced physicians are even able to determine the leukemia subtype of the sample. However, the identification process may have error rates up to 40% (when classifying acute leukemia subtypes depending on the physician's experience and the sample quality. This problem raises the need to create automatic tools that provide hematologists with a second opinion during the classification process. Our research presents a contextual analysis methodology for the detection of acute leukemia subtypes from bone marrow cells images. We propose a cells separation algorithm to break up overlapped regions. In this phase, we achieved an average accuracy of 95% in the evaluation of the segmentation process. In a second phase, we extract descriptive features to the nucleus and cytoplasm obtained in the segmentation phase in order to classify leukemia families and subtypes. We finally created a decision algorithm that provides an automatic diagnosis for a patient. In our experiments, we achieved an overall accuracy of 92% in the supervised classification of acute leukemia families, 84% for the lymphoblastic subtypes, and 92% for the myeloblastic subtypes. Finally, we achieved accuracies of 95% in the diagnosis of leukemia families and 90% in the diagnosis of leukemia subtypes.

  9. Geographic atrophy segmentation in infrared and autofluorescent retina images using supervised learning.

    Science.gov (United States)

    Devisetti, K; Karnowski, T P; Giancardo, L; Li, Y; Chaum, E

    2011-01-01

    Geographic Atrophy (GA) of the retinal pigment epithelium (RPE) is an advanced form of atrophic age-related macular degeneration (AMD) and is responsible for about 20% of AMD-related legal blindness in the United States. Two different imaging modalities for retinas, infrared imaging and autofluorescence imaging, serve as interesting complimentary technologies for highlighting GA. In this work we explore the use of neural network classifiers in performing segmentation of GA in registered infrared (IR) and autofluorescence (AF) images. Our segmentation achieved a performance level of 82.5% sensitivity and 92.9% specificity on a per-pixel basis using hold-one-out validation testing. The algorithm, feature extraction, data set and experimental results are discussed and shown.

  10. Learning scale-variant and scale-invariant features for deep image classification

    NARCIS (Netherlands)

    van Noord, Nanne; Postma, Eric

    Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial

  11. Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

    Science.gov (United States)

    Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

    2010-02-01

    Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

  12. Analysis and Evaluation of IKONOS Image Fusion Algorithm Based on Land Cover Classification

    Institute of Scientific and Technical Information of China (English)

    Xia; JING; Yan; BAO

    2015-01-01

    Different fusion algorithm has its own advantages and limitations,so it is very difficult to simply evaluate the good points and bad points of the fusion algorithm. Whether an algorithm was selected to fuse object images was also depended upon the sensor types and special research purposes. Firstly,five fusion methods,i. e. IHS,Brovey,PCA,SFIM and Gram-Schmidt,were briefly described in the paper. And then visual judgment and quantitative statistical parameters were used to assess the five algorithms. Finally,in order to determine which one is the best suitable fusion method for land cover classification of IKONOS image,the maximum likelihood classification( MLC) was applied using the above five fusion images. The results showed that the fusion effect of SFIM transform and Gram-Schmidt transform were better than the other three image fusion methods in spatial details improvement and spectral information fidelity,and Gram-Schmidt technique was superior to SFIM transform in the aspect of expressing image details. The classification accuracy of the fused image using Gram-Schmidt and SFIM algorithms was higher than that of the other three image fusion methods,and the overall accuracy was greater than 98%. The IHS-fused image classification accuracy was the lowest,the overall accuracy and kappa coefficient were 83. 14% and 0. 76,respectively. Thus the IKONOS fusion images obtained by the Gram-Schmidt and SFIM were better for improving the land cover classification accuracy.

  13. IMPACTS OF PATCH SIZE AND LAND COVER HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    Science.gov (United States)

    Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of miss-classifying pixels during thematic image classification. However, there has been a lack of empirical evidence to support these hypotheses,...

  14. Representation learning with deep extreme learning machines for efficient image set classification

    KAUST Repository

    Uzair, Muhammad

    2016-12-09

    Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

  15. Representation learning with deep extreme learning machines for efficient image set classification

    KAUST Repository

    Uzair, Muhammad; Shafait, Faisal; Ghanem, Bernard; Mian, Ajmal

    2016-01-01

    Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

  16. A classification model of Hyperion image base on SAM combined decision tree

    Science.gov (United States)

    Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin

    2009-10-01

    Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model

  17. Three-dimensional imaging of acetabular dysplasia: diagnostic value and impact on surgical type classification

    International Nuclear Information System (INIS)

    Smet, Maria-Helena; Marchal, Guy J.; Baert, Albert L.; Hoe, Lieven van; Cleynenbreugel, Johan van; Daniels, Hans; Molenaers, Guy; Moens, Pierre; Fabry, Guy

    2000-01-01

    Objective: To investigate the diagnostic value and the impact on surgical type classification of three-dimensional (3D) images for pre-surgical evaluation of dysplastic hips. Materials and methods: Three children with a different surgical type of hip dysplasia were investigated with helical computed tomography. For each patient, two-dimensional (2D) images, 3D, and a stereolithographic model of the dysplastic hip were generated. In two separate sessions, 40 medical observers independently analyzed the 2D images (session 1), the 2D and 3D images (session 2), and tried to identify the corresponding stereolithographic hip model. The influence of both image presentation (2D versus 3D images) and observer (degree of experience, radiologist versus orthopedic surgeon) were statistically analyzed. The SL model choice reflected the impact on surgical type classification. Results: Image presentation was a significant factor whereas the individual observer was not. Three-dimensional images scored significantly better than 2D images (P=0.0003). Three-dimensional imaging increased the correct surgical type classification by 35%. Conclusion: Three-dimensional images significantly improve the pre-surgical diagnostic assessment and surgical type classification of dysplastic hips

  18. Three-dimensional imaging of acetabular dysplasia: diagnostic value and impact on surgical type classification

    Energy Technology Data Exchange (ETDEWEB)

    Smet, Maria-Helena E-mail: marleen.smet@uz.kuleuven.ac.be; Marchal, Guy J.; Baert, Albert L.; Hoe, Lieven van; Cleynenbreugel, Johan van; Daniels, Hans; Molenaers, Guy; Moens, Pierre; Fabry, Guy

    2000-04-01

    Objective: To investigate the diagnostic value and the impact on surgical type classification of three-dimensional (3D) images for pre-surgical evaluation of dysplastic hips. Materials and methods: Three children with a different surgical type of hip dysplasia were investigated with helical computed tomography. For each patient, two-dimensional (2D) images, 3D, and a stereolithographic model of the dysplastic hip were generated. In two separate sessions, 40 medical observers independently analyzed the 2D images (session 1), the 2D and 3D images (session 2), and tried to identify the corresponding stereolithographic hip model. The influence of both image presentation (2D versus 3D images) and observer (degree of experience, radiologist versus orthopedic surgeon) were statistically analyzed. The SL model choice reflected the impact on surgical type classification. Results: Image presentation was a significant factor whereas the individual observer was not. Three-dimensional images scored significantly better than 2D images (P=0.0003). Three-dimensional imaging increased the correct surgical type classification by 35%. Conclusion: Three-dimensional images significantly improve the pre-surgical diagnostic assessment and surgical type classification of dysplastic hips.

  19. Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

    Directory of Open Access Journals (Sweden)

    Soheila Gheisari

    2018-01-01

    Full Text Available Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.

  20. Classification of MR brain images by combination of multi-CNNs for AD diagnosis

    Science.gov (United States)

    Cheng, Danni; Liu, Manhua; Fu, Jianliang; Wang, Yaping

    2017-07-01

    Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for development of future treatment. Magnetic resonance images (MRI) play important role to help understand the brain anatomical changes related to AD. Conventional methods extract the hand-crafted features such as gray matter volumes and cortical thickness and train a classifier to distinguish AD from other groups. Different from these methods, this paper proposes to construct multiple deep 3D convolutional neural networks (3D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. First, a number of local image patches are extracted from the whole brain image and a 3D-CNN is built upon each local patch to transform the local image into more compact high-level features. Then, the upper convolution and fully connected layers are fine-tuned to combine the multiple 3D-CNNs for image classification. The proposed method can automatically learn the generic features from imaging data for classification. Our method is evaluated using T1-weighted structural MR brain images on 428 subjects including 199 AD patients and 229 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 87.15% and an AUC (area under the ROC curve) of 92.26% for AD classification, demonstrating the promising classification performances.

  1. Satellite Image Classification of Building Damages Using Airborne and Satellite Image Samples in a Deep Learning Approach

    Science.gov (United States)

    Duarte, D.; Nex, F.; Kerle, N.; Vosselman, G.

    2018-05-01

    The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4 % the overall satellite image

  2. Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

    Science.gov (United States)

    Wutsqa, D. U.; Marwah, M.

    2017-06-01

    In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

  3. REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA

    Directory of Open Access Journals (Sweden)

    X. Yu

    2016-06-01

    Full Text Available Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC and Maximum Likelihood Classification Method (MLC in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

  4. Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

    Directory of Open Access Journals (Sweden)

    Ming-Der Yang

    2017-06-01

    Full Text Available Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV imagery. In addition to spectral information, digital surface model (DSM and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%. The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment.

  5. BIOCAT: a pattern recognition platform for customizable biological image classification and annotation.

    Science.gov (United States)

    Zhou, Jie; Lamichhane, Santosh; Sterne, Gabriella; Ye, Bing; Peng, Hanchuan

    2013-10-04

    Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be "chained" in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological

  6. Whither Supervision?

    OpenAIRE

    Duncan Waite

    2006-01-01

    This paper inquires if the school supervision is in decadence. Dr. Waite responds that the answer will depend on which perspective you look at it. Dr. Waite suggests taking in consideration three elements that are related: the field itself, the expert in the field (the professor, the theorist, the student and the administrator), and the context. When these three elements are revised, it emphasizes that there is not a consensus about the field of supervision, but there are coincidences related...

  7. Comparison and Supervised Learning of Segmentation Methods Dedicated to Specular Microscope Images of Corneal Endothelium

    Directory of Open Access Journals (Sweden)

    Yann Gavet

    2014-01-01

    Full Text Available The cornea is the front of the eye. Its inner cell layer, called the endothelium, is important because it is closely related to the light transparency of the cornea. An in vivo observation of this layer is performed by using specular microscopy to evaluate the health of the cells: a high spatial density will result in a good transparency. Thus, the main criterion required by ophthalmologists is the cell density of the cornea endothelium, mainly obtained by an image segmentation process. Different methods can perform the image segmentation of these cells, and the three most performing methods are studied here. The question for the ophthalmologists is how to choose the best algorithm and to obtain the best possible results with it. This paper presents a methodology to compare these algorithms together. Moreover, by the way of geometric dissimilarity criteria, the algorithms are tuned up, and the best parameter values are thus proposed to the expert ophthalmologists.

  8. Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning

    OpenAIRE

    Lai, Rongjie; Li, Jia

    2017-01-01

    Low-rank structures play important role in recent advances of many problems in image science and data science. As a natural extension of low-rank structures for data with nonlinear structures, the concept of the low-dimensional manifold structure has been considered in many data processing problems. Inspired by this concept, we consider a manifold based low-rank regularization as a linear approximation of manifold dimension. This regularization is less restricted than the global low-rank regu...

  9. A supervised contextual classifier based on a region-growth algorithm

    DEFF Research Database (Denmark)

    Lira, Jorge; Maletti, Gabriela Mariel

    2002-01-01

    A supervised classification scheme to segment optical multi-spectral images has been developed. In this classifier, an automated region-growth algorithm delineates the training sets. This algorithm handles three parameters: an initial pixel seed, a window size and a threshold for each class. A su...

  10. Multi-material classification of dry recyclables from municipal solid waste based on thermal imaging.

    Science.gov (United States)

    Gundupalli, Sathish Paulraj; Hait, Subrata; Thakur, Atul

    2017-12-01

    There has been a significant rise in municipal solid waste (MSW) generation in the last few decades due to rapid urbanization and industrialization. Due to the lack of source segregation practice, a need for automated segregation of recyclables from MSW exists in the developing countries. This paper reports a thermal imaging based system for classifying useful recyclables from simulated MSW sample. Experimental results have demonstrated the possibility to use thermal imaging technique for classification and a robotic system for sorting of recyclables in a single process step. The reported classification system yields an accuracy in the range of 85-96% and is comparable with the existing single-material recyclable classification techniques. We believe that the reported thermal imaging based system can emerge as a viable and inexpensive large-scale classification-cum-sorting technology in recycling plants for processing MSW in developing countries. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification.

    Science.gov (United States)

    Bing, Lu; Wang, Wei

    2017-01-01

    We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

  12. A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

    Science.gov (United States)

    Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

    2016-01-01

    Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520

  13. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Science.gov (United States)

    Huo, Guanying

    2017-01-01

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

  14. A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

    Directory of Open Access Journals (Sweden)

    Guizhou Wang

    2013-01-01

    Full Text Available This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine. Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.

  15. Classification of visualization exudates fundus images results using ...

    African Journals Online (AJOL)

    The kernel function settings; linear, polynomial, quadratic and RBF have an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using ts accuracy. It has proven to discriminated exudates and non ...

  16. Visual Perception-Based Statistical Modeling of Complex Grain Image for Product Quality Monitoring and Supervision on Assembly Production Line.

    Directory of Open Access Journals (Sweden)

    Jinping Liu

    Full Text Available Computer vision as a fast, low-cost, noncontact, and online monitoring technology has been an important tool to inspect product quality, particularly on a large-scale assembly production line. However, the current industrial vision system is far from satisfactory in the intelligent perception of complex grain images, comprising a large number of local homogeneous fragmentations or patches without distinct foreground and background. We attempt to solve this problem based on the statistical modeling of spatial structures of grain images. We present a physical explanation in advance to indicate that the spatial structures of the complex grain images are subject to a representative Weibull distribution according to the theory of sequential fragmentation, which is well known in the continued comminution of ore grinding. To delineate the spatial structure of the grain image, we present a method of multiscale and omnidirectional Gaussian derivative filtering. Then, a product quality classifier based on sparse multikernel-least squares support vector machine is proposed to solve the low-confidence classification problem of imbalanced data distribution. The proposed method is applied on the assembly line of a food-processing enterprise to classify (or identify automatically the production quality of rice. The experiments on the real application case, compared with the commonly used methods, illustrate the validity of our method.

  17. Visual Perception-Based Statistical Modeling of Complex Grain Image for Product Quality Monitoring and Supervision on Assembly Production Line.

    Science.gov (United States)

    Liu, Jinping; Tang, Zhaohui; Zhang, Jin; Chen, Qing; Xu, Pengfei; Liu, Wenzhong

    2016-01-01

    Computer vision as a fast, low-cost, noncontact, and online monitoring technology has been an important tool to inspect product quality, particularly on a large-scale assembly production line. However, the current industrial vision system is far from satisfactory in the intelligent perception of complex grain images, comprising a large number of local homogeneous fragmentations or patches without distinct foreground and background. We attempt to solve this problem based on the statistical modeling of spatial structures of grain images. We present a physical explanation in advance to indicate that the spatial structures of the complex grain images are subject to a representative Weibull distribution according to the theory of sequential fragmentation, which is well known in the continued comminution of ore grinding. To delineate the spatial structure of the grain image, we present a method of multiscale and omnidirectional Gaussian derivative filtering. Then, a product quality classifier based on sparse multikernel-least squares support vector machine is proposed to solve the low-confidence classification problem of imbalanced data distribution. The proposed method is applied on the assembly line of a food-processing enterprise to classify (or identify) automatically the production quality of rice. The experiments on the real application case, compared with the commonly used methods, illustrate the validity of our method.

  18. Classification of high-resolution remote sensing images based on multi-scale superposition

    Science.gov (United States)

    Wang, Jinliang; Gao, Wenjie; Liu, Guangjie

    2017-07-01

    Landscape structures and process on different scale show different characteristics. In the study of specific target landmarks, the most appropriate scale for images can be attained by scale conversion, which improves the accuracy and efficiency of feature identification and classification. In this paper, the authors carried out experiments on multi-scale classification by taking the Shangri-la area in the north-western Yunnan province as the research area and the images from SPOT5 HRG and GF-1 Satellite as date sources. Firstly, the authors upscaled the two images by cubic convolution, and calculated the optimal scale for different objects on the earth shown in images by variation functions. Then the authors conducted multi-scale superposition classification on it by Maximum Likelyhood, and evaluated the classification accuracy. The results indicates that: (1) for most of the object on the earth, the optimal scale appears in the bigger scale instead of the original one. To be specific, water has the biggest optimal scale, i.e. around 25-30m; farmland, grassland, brushwood, roads, settlement places and woodland follows with 20-24m. The optimal scale for shades and flood land is basically as the same as the original one, i.e. 8m and 10m respectively. (2) Regarding the classification of the multi-scale superposed images, the overall accuracy of the ones from SPOT5 HRG and GF-1 Satellite is 12.84% and 14.76% higher than that of the original multi-spectral images, respectively, and Kappa coefficient is 0.1306 and 0.1419 higher, respectively. Hence, the multi-scale superposition classification which was applied in the research area can enhance the classification accuracy of remote sensing images .

  19. A minimum spanning forest based classification method for dedicated breast CT images

    International Nuclear Information System (INIS)

    Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei

    2015-01-01

    Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging

  20. Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning

    Directory of Open Access Journals (Sweden)

    Liu Ying

    2017-01-01

    Full Text Available Ship detection and classification is critical for national maritime security and national defense. Although some SAR (Synthetic Aperture Radar image-based ship detection approaches have been proposed and used, they are not able to satisfy the requirement of real-world applications as the number of SAR sensors is limited, the resolution is low, and the revisit cycle is long. As massive optical remote sensing images of high resolution are available, ship detection and classification on theses images is becoming a promising technique, and has attracted great attention on applications including maritime security and traffic control. Some digital image processing methods have been proposed to detect ships in optical remote sensing images, but most of them face difficulty in terms of accuracy, performance and complexity. Recently, an autoencoder-based deep neural network with extreme learning machine was proposed, but it cannot meet the requirement of real-world applications as it only works with simple and small-scaled data sets. Therefore, in this paper, we propose a novel ship detection and classification approach which utilizes deep convolutional neural network (CNN as the ship classifier. The performance of our proposed ship detection and classification approach was evaluated on a set of images downloaded from Google Earth at the resolution 0.5m. 99% detection accuracy and 95% classification accuracy were achieved. In model training, 75× speedup is achieved on 1 Nvidia Titanx GPU.

  1. Radiomic features analysis in computed tomography images of lung nodule classification.

    Directory of Open Access Journals (Sweden)

    Chia-Hung Chen

    Full Text Available Radiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction.Lung nodule classification using radiomics based on Computed Tomography (CT image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist.Among the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%.The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.

  2. Multi-Scale Pattern Recognition for Image Classification and Segmentation

    NARCIS (Netherlands)

    Li, Y.

    2013-01-01

    Scale is an important parameter of images. Different objects or image structures (e.g. edges and corners) can appear at different scales and each is meaningful only over a limited range of scales. Multi-scale analysis has been widely used in image processing and computer vision, serving as the basis

  3. Visual Analytics Applied to Image Analysis : From Segmentation to Classification

    NARCIS (Netherlands)

    Rauber, Paulo

    2017-01-01

    Image analysis is the field of study concerned with extracting information from images. This field is immensely important for commercial and scientific applications, from identifying people in photographs to recognizing diseases in medical images. The goal behind the work presented in this thesis is

  4. Distance-Based Image Classification: Generalizing to New Classes at Near Zero Cost

    NARCIS (Netherlands)

    Mensink, T.; Verbeek, J.; Perronnin, F.; Csurka, G.

    2013-01-01

    We study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end, we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, and introduce a new

  5. Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets

    NARCIS (Netherlands)

    Mensink, T.; Verbeek, J.; Perronnin, F.; Csurka, G.; Farinella, G.M.; Battiato, S.; Cipolla, R,

    2013-01-01

    Many real-life large-scale datasets are open-ended and dynamic: new images are continuously added to existing classes, new classes appear over time, and the semantics of existing classes might evolve too. Therefore, we study large-scale image classification methods that can incorporate new classes

  6. Hyperspectral Image Enhancement and Mixture Deep-Learning Classification of Corneal Epithelium Injuries.

    Science.gov (United States)

    Noor, Siti Salwa Md; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

    2017-11-16

    In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear. The performance results indicate that our chosen image features from the histogram and length-scale parameter were able to classify with up to 100% accuracy; particularly, at CNNs and CNNs-SVM, by employing 80% of the data sample for training and 20% for testing. Thus, in the assessment of corneal epithelium injuries, HSI has high potential as a method that could surpass current technologies regarding speed, objectivity, and reliability.

  7. Multi-Pixel Simultaneous Classification of PolSAR Image Using Convolutional Neural Networks

    Science.gov (United States)

    Xu, Xin; Gui, Rong; Pu, Fangling

    2018-01-01

    Convolutional neural networks (CNN) have achieved great success in the optical image processing field. Because of the excellent performance of CNN, more and more methods based on CNN are applied to polarimetric synthetic aperture radar (PolSAR) image classification. Most CNN-based PolSAR image classification methods can only classify one pixel each time. Because all the pixels of a PolSAR image are classified independently, the inherent interrelation of different land covers is ignored. We use a fixed-feature-size CNN (FFS-CNN) to classify all pixels in a patch simultaneously. The proposed method has several advantages. First, FFS-CNN can classify all the pixels in a small patch simultaneously. When classifying a whole PolSAR image, it is faster than common CNNs. Second, FFS-CNN is trained to learn the interrelation of different land covers in a patch, so it can use the interrelation of land covers to improve the classification results. The experiments of FFS-CNN are evaluated on a Chinese Gaofen-3 PolSAR image and other two real PolSAR images. Experiment results show that FFS-CNN is comparable with the state-of-the-art PolSAR image classification methods. PMID:29510499

  8. A Public Image Database for Benchmark of Plant Seedling Classification Algorithms

    DEFF Research Database (Denmark)

    Giselsson, Thomas Mosgaard; Nyholm Jørgensen, Rasmus; Jensen, Peter Kryger

    A database of images of approximately 960 unique plants belonging to 12 species at several growth stages is made publicly available. It comprises annotated RGB images with a physical resolution of roughly 10 pixels per mm. To standardise the evaluation of classification results obtained...

  9. Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification

    Energy Technology Data Exchange (ETDEWEB)

    Drukker, Karen, E-mail: kdrukker@uchicago.edu; Giger, Maryellen L.; Li, Hui [Department of Radiology, University of Chicago, Chicago, Illinois 60637 (United States); Duewer, Fred; Malkov, Serghei; Joe, Bonnie; Kerlikowske, Karla; Shepherd, John A. [Radiology Department, University of California, San Francisco, California 94143 (United States); Flowers, Chris I. [Department of Radiology, University of South Florida, Tampa, Florida 33612 (United States); Drukteinis, Jennifer S. [Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612 (United States)

    2014-03-15

    Purpose: To investigate whether biologic image composition of mammographic lesions can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy. Methods: The study population consisted of 45 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 10 invasive ductal carcinomas, 5 ductal carcinomain situ, 11 fibroadenomas, and 19 other benign diagnoses. Analysis was threefold: (1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, “QIA alone,” (2) the three-compartment breast (3CB) composition measure—derived from the dual-energy mammography—of water, lipid, and protein thickness were assessed, “3CB alone”, and (3) information from QIA and 3CB was combined, “QIA + 3CB.” Analysis was initiated from radiologist-indicated lesion centers and was otherwise fully automated. Steps of the QIA and 3CB methods were lesion segmentation, characterization, and subsequent classification for malignancy in leave-one-case-out cross-validation. Performance assessment included box plots, Bland–Altman plots, and Receiver Operating Characteristic (ROC) analysis. Results: The area under the ROC curve (AUC) for distinguishing between benign and malignant lesions (invasive and DCIS) was 0.81 (standard error 0.07) for the “QIA alone” method, 0.72 (0.07) for “3CB alone” method, and 0.86 (0.04) for “QIA+3CB” combined. The difference in AUC was 0.043 between “QIA + 3CB” and “QIA alone” but failed to reach statistical significance (95% confidence interval [–0.17 to + 0.26]). Conclusions: In this pilot study analyzing the new 3CB imaging modality, knowledge of the composition of breast lesions and their periphery appeared additive in combination with existing mammographic QIA methods for the distinction between different benign and malignant lesion types.

  10. Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules.

    Science.gov (United States)

    Feng, Xinyang; Yang, Jie; Laine, Andrew F; Angelini, Elsa D

    2017-09-01

    Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework. Experimental results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a fully-supervised CNN-based segmentation method.

  11. SAR Image Classification Based on Its Texture Features

    Institute of Scientific and Technical Information of China (English)

    LI Pingxiang; FANG Shenghui

    2003-01-01

    SAR images not only have the characteristics of all-ay, all-eather, but also provide object information which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The authors conducted the experiments of texture statistics analysis on SAR image features in order to improve the accuracy of SAR image interpretation.It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpretation.

  12. Integrating image processing and classification technology into automated polarizing film defect inspection

    Science.gov (United States)

    Kuo, Chung-Feng Jeffrey; Lai, Chun-Yu; Kao, Chih-Hsiang; Chiu, Chin-Hsun

    2018-05-01

    In order to improve the current manual inspection and classification process for polarizing film on production lines, this study proposes a high precision automated inspection and classification system for polarizing film, which is used for recognition and classification of four common defects: dent, foreign material, bright spot, and scratch. First, the median filter is used to remove the impulse noise in the defect image of polarizing film. The random noise in the background is smoothed by the improved anisotropic diffusion, while the edge detail of the defect region is sharpened. Next, the defect image is transformed by Fourier transform to the frequency domain, combined with a Butterworth high pass filter to sharpen the edge detail of the defect region, and brought back by inverse Fourier transform to the spatial domain to complete the image enhancement process. For image segmentation, the edge of the defect region is found by Canny edge detector, and then the complete defect region is obtained by two-stage morphology processing. For defect classification, the feature values, including maximum gray level, eccentricity, the contrast, and homogeneity of gray level co-occurrence matrix (GLCM) extracted from the images, are used as the input of the radial basis function neural network (RBFNN) and back-propagation neural network (BPNN) classifier, 96 defect images are then used as training samples, and 84 defect images are used as testing samples to validate the classification effect. The result shows that the classification accuracy by using RBFNN is 98.9%. Thus, our proposed system can be used by manufacturing companies for a higher yield rate and lower cost. The processing time of one single image is 2.57 seconds, thus meeting the practical application requirement of an industrial production line.

  13. Image Analysis and Classification Based on Soil Strength

    Science.gov (United States)

    2016-08-01

    Impact Hammer, which is light, easy to operate, and cost effective . The Clegg Impact Hammer measures stiffness of the soil surface by drop- ping a... effect on out-of-scene classifications. More statistical analy- sis should, however, be done to compare the measured field spectra, the WV2 training...DISCLAIMER: The contents of this report are not to be used for advertising , publication, or promotional purposes. Ci- tation of trade names does not

  14. Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning

    Directory of Open Access Journals (Sweden)

    Chunyang Wang

    2015-01-01

    Full Text Available Study on land use/cover can reflect changing rules of population, economy, agricultural structure adjustment, policy, and traffic and provide better service for the regional economic development and urban evolution. The study on fine land use/cover assessment using hyperspectral image classification is a focal growing area in many fields. Semisupervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively, has been a new research direction. In this paper, we proposed improving fine land use/cover assessment based on semisupervised hyperspectral classification method. The test analysis of study area showed that the advantages of semisupervised classification method could improve the high precision overall classification and objective assessment of land use/cover results.

  15. Voice classification and vocal tract of singers: a study of x-ray images and morphology.

    Science.gov (United States)

    Roers, Friederike; Mürbe, Dirk; Sundberg, Johan

    2009-01-01

    This investigation compares vocal tract dimensions and the classification of singer voices by examining an x-ray material assembled between 1959 and 1991 of students admitted to the solo singing education at the University of Music, Dresden, Germany. A total of 132 images were available to analysis. Different classifications' values of the lengths of the total vocal tract, the pharynx, and mouth cavities as well as of the relative position of the larynx, the height of the palatal arch, and the estimated vocal fold length were analyzed statistically, and some significant differences were found. The length of the pharynx cavity seemed particularly influential on the total vocal tract length, which varied systematically with classification. Also studied were the relationships between voice classification and the body height and weight and the body mass index. The data support the hypothesis that there are consistent morphological vocal tract differences between singers of different voice classifications.

  16. Automatic classification and detection of clinically relevant images for diabetic retinopathy

    Science.gov (United States)

    Xu, Xinyu; Li, Baoxin

    2008-03-01

    We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

  17. UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

    Directory of Open Access Journals (Sweden)

    A. Kianisarkaleh

    2015-12-01

    Full Text Available Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.

  18. Astrophysical Information from Objective Prism Digitized Images: Classification with an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Bratsolis Emmanuel

    2005-01-01

    Full Text Available Stellar spectral classification is not only a tool for labeling individual stars but is also useful in studies of stellar population synthesis. Extracting the physical quantities from the digitized spectral plates involves three main stages: detection, extraction, and classification of spectra. Low-dispersion objective prism images have been used and automated methods have been developed. The detection and extraction problems have been presented in previous works. In this paper, we present a classification method based on an artificial neural network (ANN. We make a brief presentation of the entire automated system and we compare the new classification method with the previously used method of maximum correlation coefficient (MCC. Digitized photographic material has been used here. The method can also be used on CCD spectral images.

  19. Application of SVM classifier in thermographic image classification for early detection of breast cancer

    Science.gov (United States)

    Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał

    2016-09-01

    This article presents the application of machine learning algorithms for early detection of breast cancer on the basis of thermographic images. Supervised learning model: Support vector machine (SVM) and Sequential Minimal Optimization algorithm (SMO) for the training of SVM classifier were implemented. The SVM classifier was included in a client-server application which enables to create a training set of examinations and to apply classifiers (including SVM) for the diagnosis and early detection of the breast cancer. The sensitivity and specificity of SVM classifier were calculated based on the thermographic images from studies. Furthermore, the heuristic method for SVM's parameters tuning was proposed.

  20. Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery

    Directory of Open Access Journals (Sweden)

    Harvey Neal R

    2007-07-01

    Full Text Available Abstract Background We present an analysis of the utility of multispectral versus standard RGB imagery for routine H&E stained histopathology images, in particular for pixel-level classification of nuclei. Our multispectral imagery has 29 spectral bands, spaced 10 nm within the visual range of 420–700 nm. It has been hypothesized that the additional spectral bands contain further information useful for classification as compared to the 3 standard bands of RGB imagery. We present analyses of our data designed to test this hypothesis. Results For classification using all available image bands, we find the best performance (equal tradeoff between detection rate and false alarm rate is obtained from either the multispectral or our "ccd" RGB imagery, with an overall increase in performance of 0.79% compared to the next best performing image type. For classification using single image bands, the single best multispectral band (in the red portion of the spectrum gave a performance increase of 0.57%, compared to performance of the single best RGB band (red. Additionally, red bands had the highest coefficients/preference in our classifiers. Principal components analysis of the multispectral imagery indicates only two significant image bands, which is not surprising given the presence of two stains. Conclusion Our results indicate that multispectral imagery for routine H&E stained histopathology provides minimal additional spectral information for a pixel-level nuclear classification task than would standard RGB imagery.

  1. APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF HIGH RESOLUTION AGRICULTURAL REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    C. Yao

    2017-09-01

    Full Text Available With the rapid development of Precision Agriculture (PA promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN. For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.

  2. Whither Supervision?

    Directory of Open Access Journals (Sweden)

    Duncan Waite

    2006-11-01

    Full Text Available This paper inquires if the school supervision is in decadence. Dr. Waite responds that the answer will depend on which perspective you look at it. Dr. Waite suggests taking in consideration three elements that are related: the field itself, the expert in the field (the professor, the theorist, the student and the administrator, and the context. When these three elements are revised, it emphasizes that there is not a consensus about the field of supervision, but there are coincidences related to its importance and that it is related to the improvement of the practice of the students in the school for their benefit. Dr. Waite suggests that the practice on this field is not always in harmony with what the theorists affirm. When referring to the supervisor or the skilled person, the author indicates that his or her perspective depends on his or her epistemological believes or in the way he or she conceives the learning; that is why supervision can be understood in different ways. About the context, Waite suggests that there have to be taken in consideration the social or external forces that influent the people and the society, because through them the education is affected. Dr. Waite concludes that the way to understand the supervision depends on the performer’s perspective. He responds to the initial question saying that the supervision authorities, the knowledge on this field, the performers, and its practice, are maybe spread but not extinct because the supervision will always be part of the great enterprise that we called education.

  3. Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

    Directory of Open Access Journals (Sweden)

    Margarita Gamarra

    2017-08-01

    Full Text Available Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

  4. Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images

    Directory of Open Access Journals (Sweden)

    Yongguang Zhai

    2018-03-01

    Full Text Available Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for mining the effective features to identify different land cover types. Therefore, a classification method that can take full advantage of the rich information provided by time-series data to improve the accuracy of land cover classification is needed. In this paper, a novel method for time-series land cover classification using spectral, temporal, and spatial information at an annual scale was introduced. Based on all the available data from time-series remote sensing images, a refined nonlinear dimensionality reduction method was used to extract the spectral and temporal features, and a modified graph segmentation method was used to extract the spatial features. The proposed classification method was applied in three study areas with land cover complexity, including Illinois, South Dakota, and Texas. All the Landsat time series data in 2014 were used, and different study areas have different amounts of invalid data. A series of comparative experiments were conducted on the annual time-series images using training data generated from Cropland Data Layer. The results demonstrated higher overall and per-class classification accuracies and kappa index values using the proposed spectral-temporal-spatial method compared to spectral-temporal classification methods. We also discuss the implications of this study and possibilities for future applications and developments of the method.

  5. The k-Language Classification, a Proposed New Theory for Image Classification and Clustering at Pixel Level

    Directory of Open Access Journals (Sweden)

    Alwi Aslan

    2014-03-01

    Full Text Available This theory attempted to explore the possibility of using regular language further in image analysis, departing from the use of string to represent the region in the image. But we are not trying to show an alternative idea about how to generate a string region, where there are many different ways how the image or region produces strings representing, in this paper we propose a way how to generate regular language or group of languages which performs both classify the set of strings generated by a group of a number of image regions. Researchers began by showing a proof that there is always a regular language that accepts a set of strings that produced the image, and then use the language to perform the classification. Research then expanded to the pixel level, on whether the regular language can be used for clustering pixels in the image, the researchers propose a systematic solution of this question. As a tool used to explore regular language is deterministic finite automata. On the end part before conclusion of this paper, we add revision version of this theory. There is another point of view to revision version, added for make this method more precision and more powerfull from before.

  6. Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning.

    Science.gov (United States)

    Liu, Jinping; Tang, Zhaohui; Xu, Pengfei; Liu, Wenzhong; Zhang, Jin; Zhu, Jianyong

    2016-06-29

    The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images' spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines.

  7. GLOBAL CLASSIFICATION OF DERMATITIS DISEASE WITH K-MEANS CLUSTERING IMAGE SEGMENTATION METHODS

    OpenAIRE

    Prafulla N. Aerkewar1 & Dr. G. H. Agrawal2

    2018-01-01

    The objective of this paper to presents a global technique for classification of different dermatitis disease lesions using the process of k-Means clustering image segmentation method. The word global is used such that the all dermatitis disease having skin lesion on body are classified in to four category using k-means image segmentation and nntool of Matlab. Through the image segmentation technique and nntool can be analyze and study the segmentation properties of skin lesions occurs in...

  8. Rule-based land cover classification from very high-resolution satellite image with multiresolution segmentation

    Science.gov (United States)

    Haque, Md. Enamul; Al-Ramadan, Baqer; Johnson, Brian A.

    2016-07-01

    Multiresolution segmentation and rule-based classification techniques are used to classify objects from very high-resolution satellite images of urban areas. Custom rules are developed using different spectral, geometric, and textural features with five scale parameters, which exploit varying classification accuracy. Principal component analysis is used to select the most important features out of a total of 207 different features. In particular, seven different object types are considered for classification. The overall classification accuracy achieved for the rule-based method is 95.55% and 98.95% for seven and five classes, respectively. Other classifiers that are not using rules perform at 84.17% and 97.3% accuracy for seven and five classes, respectively. The results exploit coarse segmentation for higher scale parameter and fine segmentation for lower scale parameter. The major contribution of this research is the development of rule sets and the identification of major features for satellite image classification where the rule sets are transferable and the parameters are tunable for different types of imagery. Additionally, the individual objectwise classification and principal component analysis help to identify the required object from an arbitrary number of objects within images given ground truth data for the training.

  9. Multi-spectral Image Analysis for Astaxanthin Coating Classification

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht

    2011-01-01

    Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. In this study multi-spectral image analysis of pellets was performed using LDA, QDA, SNV and PCA on pixel level and mean value of pixels...

  10. Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification.

    Science.gov (United States)

    Diamant, Idit; Klang, Eyal; Amitai, Michal; Konen, Eli; Goldberger, Jacob; Greenspan, Hayit

    2017-06-01

    We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification. Our approach improves the BoVW model by learning a task-driven dictionary of the most relevant visual words per task using a mutual information-based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words. We applied our algorithm to three different tasks: chest x-ray pathology identification (of four pathologies: cardiomegaly, enlarged mediastinum, right consolidation, and left consolidation), liver lesion classification into four categories in computed tomography (CT) images and benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms. Validation was conducted on three datasets: 443 chest x-rays, 118 portal phase CT images of liver lesions, and 260 mammography MCs. The proposed method improves the classical BoVW method for all tested applications. For chest x-ray, area under curve of 0.876 was obtained for enlarged mediastinum identification compared to 0.855 using classical BoVW (with p-value 0.01). For MC classification, a significant improvement of 4% was achieved using our new approach (with p-value = 0.03). For liver lesion classification, an improvement of 6% in sensitivity and 2% in specificity were obtained (with p-value 0.001). We demonstrated that classification based on informative selected set of words results in significant improvement. Our new BoVW approach shows promising results in clinically important domains. Additionally, it can discover relevant parts of images for the task at hand without explicit annotations for training data. This can provide computer-aided support for medical experts in challenging image analysis tasks.

  11. Multimodal Task-Driven Dictionary Learning for Image Classification

    Science.gov (United States)

    2015-12-18

    recognition, multi-view face recognition, multi-view action recognition, and multimodal biometric recognition. It is also shown that, compared to the...improvement in several multi-task learning applications such as target classification, biometric recognitions, and multiview face recognition [12], [14], [17...relevant samples from other modalities for a given unimodal query. However, α1 α2 …αS D1 … Index finger Thumb finger … Iris x1 x2 xS D2 DS … … … J o in

  12. Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

    Science.gov (United States)

    Sinha, S K; Karray, F

    2002-01-01

    Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

  13. Classification in hyperspectral images by independent component analysis, segmented cross-validation and uncertainty estimates

    Directory of Open Access Journals (Sweden)

    Beatriz Galindo-Prieto

    2018-02-01

    Full Text Available Independent component analysis combined with various strategies for cross-validation, uncertainty estimates by jack-knifing and critical Hotelling’s T2 limits estimation, proposed in this paper, is used for classification purposes in hyperspectral images. To the best of our knowledge, the combined approach of methods used in this paper has not been previously applied to hyperspectral imaging analysis for interpretation and classification in the literature. The data analysis performed here aims to distinguish between four different types of plastics, some of them containing brominated flame retardants, from their near infrared hyperspectral images. The results showed that the method approach used here can be successfully used for unsupervised classification. A comparison of validation approaches, especially leave-one-out cross-validation and regions of interest scheme validation is also evaluated.

  14. Remote classification from an airborne camera using image super-resolution.

    Science.gov (United States)

    Woods, Matthew; Katsaggelos, Aggelos

    2017-02-01

    The image processing technique known as super-resolution (SR), which attempts to increase the effective pixel sampling density of a digital imager, has gained rapid popularity over the last decade. The majority of literature focuses on its ability to provide results that are visually pleasing to a human observer. In this paper, we instead examine the ability of SR to improve the resolution-critical capability of an imaging system to perform a classification task from a remote location, specifically from an airborne camera. In order to focus the scope of the study, we address and quantify results for the narrow case of text classification. However, we expect the results generalize to a large set of related, remote classification tasks. We generate theoretical results through simulation, which are corroborated by experiments with a camera mounted on a DJI Phantom 3 quadcopter.

  15. Classification of objects on hyperspectral images — further developments

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.; Williams, Paul

    Classification of objects (such as tablets, cereals, fruits, etc.) is one of the very important applications of hyperspectral imaging and image analysis. Quite often, a hyperspectral image is represented and analyzed just as a bunch of spectra without taking into account spatial information about...... the pixels, which makes classification objects inefficient. Recently, several methods, which combine spectral and spatial information, has been also developed and this approach becomes more and more wide-spread. The methods use local rank, topology, spectral features calculated for separate objects and other...... spatial characteristics. In this work we would like to show several improvements to the classification method, which utilizes spectral features calculated for individual objects [1]. The features are based (in general) on descriptors of spatial patterns of individual object’s pixels in a common principal...

  16. Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

    Science.gov (United States)

    Hong, Liang

    2013-10-01

    The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

  17. Comparison of classification algorithms for various methods of preprocessing radar images of the MSTAR base

    Science.gov (United States)

    Borodinov, A. A.; Myasnikov, V. V.

    2018-04-01

    The present work is devoted to comparing the accuracy of the known qualification algorithms in the task of recognizing local objects on radar images for various image preprocessing methods. Preprocessing involves speckle noise filtering and normalization of the object orientation in the image by the method of image moments and by a method based on the Hough transform. In comparison, the following classification algorithms are used: Decision tree; Support vector machine, AdaBoost, Random forest. The principal component analysis is used to reduce the dimension. The research is carried out on the objects from the base of radar images MSTAR. The paper presents the results of the conducted studies.

  18. Collaborative classification of hyperspectral and visible images with convolutional neural network

    Science.gov (United States)

    Zhang, Mengmeng; Li, Wei; Du, Qian

    2017-10-01

    Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

  19. STAR-GALAXY CLASSIFICATION IN MULTI-BAND OPTICAL IMAGING

    International Nuclear Information System (INIS)

    Fadely, Ross; Willman, Beth; Hogg, David W.

    2012-01-01

    Ground-based optical surveys such as PanSTARRS, DES, and LSST will produce large catalogs to limiting magnitudes of r ∼> 24. Star-galaxy separation poses a major challenge to such surveys because galaxies—even very compact galaxies—outnumber halo stars at these depths. We investigate photometric classification techniques on stars and galaxies with intrinsic FWHM best ) where the training data are (unrealistically) a random sampling of the data in both signal-to-noise and demographics and (2) a more realistic scenario where training is done on higher signal-to-noise data (SVM real ) at brighter apparent magnitudes. Testing with COSMOS ugriz data, we find that HB outperforms ML, delivering ∼80% completeness, with purity of ∼60%-90% for both stars and galaxies. We find that no algorithm delivers perfect performance and that studies of metal-poor main-sequence turnoff stars may be challenged by poor star-galaxy separation. Using the Receiver Operating Characteristic curve, we find a best-to-worst ranking of SVM best , HB, ML, and SVM real . We conclude, therefore, that a well-trained SVM will outperform template-fitting methods. However, a normally trained SVM performs worse. Thus, HB template fitting may prove to be the optimal classification method in future surveys.

  20. Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images

    Science.gov (United States)

    Rougier, Simon; Puissant, Anne; Stumpf, André; Lachiche, Nicolas

    2016-09-01

    Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed.

  1. Rock images classification by using deep convolution neural network

    Science.gov (United States)

    Cheng, Guojian; Guo, Wenhui

    2017-08-01

    Granularity analysis is one of the most essential issues in authenticate under microscope. To improve the efficiency and accuracy of traditional manual work, an convolutional neural network based method is proposed for granularity analysis from thin section image, which chooses and extracts features from image samples while build classifier to recognize granularity of input image samples. 4800 samples from Ordos basin are used for experiments under colour spaces of HSV, YCbCr and RGB respectively. On the test dataset, the correct rate in RGB colour space is 98.5%, and it is believable in HSV and YCbCr colour space. The results show that the convolution neural network can classify the rock images with high reliability.

  2. Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

    Directory of Open Access Journals (Sweden)

    Xian-Hua Han

    2011-01-01

    extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

  3. Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network.

    Science.gov (United States)

    Chi, Jianning; Walia, Ekta; Babyn, Paul; Wang, Jimmy; Groot, Gary; Eramian, Mark

    2017-08-01

    With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into "malignant" and "benign" cases. The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.

  4. Supervised Transfer Sparse Coding

    KAUST Repository

    Al-Shedivat, Maruan

    2014-07-27

    A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

  5. Combining low level features and visual attributes for VHR remote sensing image classification

    Science.gov (United States)

    Zhao, Fumin; Sun, Hao; Liu, Shuai; Zhou, Shilin

    2015-12-01

    Semantic classification of very high resolution (VHR) remote sensing images is of great importance for land use or land cover investigation. A large number of approaches exploiting different kinds of low level feature have been proposed in the literature. Engineers are often frustrated by their conclusions and a systematic assessment of various low level features for VHR remote sensing image classification is needed. In this work, we firstly perform an extensive evaluation of eight features including HOG, dense SIFT, SSIM, GIST, Geo color, LBP, Texton and Tiny images for classification of three public available datasets. Secondly, we propose to transfer ground level scene attributes to remote sensing images. Thirdly, we combine both low-level features and mid-level visual attributes to further improve the classification performance. Experimental results demonstrate that i) Dene SIFT and HOG features are more robust than other features for VHR scene image description. ii) Visual attribute competes with a combination of low level features. iii) Multiple feature combination achieves the best performance under different settings.

  6. A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

    Science.gov (United States)

    Chang, Lan; Zhang, Mengmeng; Li, Wei

    2017-10-01

    A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

  7. Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

    Directory of Open Access Journals (Sweden)

    Fang Yang

    2017-01-01

    Full Text Available Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

  8. Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

    Science.gov (United States)

    Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

    2013-01-01

    The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

  9. A comparison of autonomous techniques for multispectral image analysis and classification

    Science.gov (United States)

    Valdiviezo-N., Juan C.; Urcid, Gonzalo; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso

    2012-10-01

    Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses.

  10. Automatic plankton image classification combining multiple view features via multiple kernel learning.

    Science.gov (United States)

    Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

    2017-12-28

    Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

  11. HEp-2 cell image classification method based on very deep convolutional networks with small datasets

    Science.gov (United States)

    Lu, Mengchi; Gao, Long; Guo, Xifeng; Liu, Qiang; Yin, Jianping

    2017-07-01

    Human Epithelial-2 (HEp-2) cell images staining patterns classification have been widely used to identify autoimmune diseases by the anti-Nuclear antibodies (ANA) test in the Indirect Immunofluorescence (IIF) protocol. Because manual test is time consuming, subjective and labor intensive, image-based Computer Aided Diagnosis (CAD) systems for HEp-2 cell classification are developing. However, methods proposed recently are mostly manual features extraction with low accuracy. Besides, the scale of available benchmark datasets is small, which does not exactly suitable for using deep learning methods. This issue will influence the accuracy of cell classification directly even after data augmentation. To address these issues, this paper presents a high accuracy automatic HEp-2 cell classification method with small datasets, by utilizing very deep convolutional networks (VGGNet). Specifically, the proposed method consists of three main phases, namely image preprocessing, feature extraction and classification. Moreover, an improved VGGNet is presented to address the challenges of small-scale datasets. Experimental results over two benchmark datasets demonstrate that the proposed method achieves superior performance in terms of accuracy compared with existing methods.

  12. Classification of radiolarian images with hand-crafted and deep features

    Science.gov (United States)

    Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

    2017-12-01

    Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

  13. Convolutional neural network-based classification system design with compressed wireless sensor network images.

    Science.gov (United States)

    Ahn, Jungmo; Park, JaeYeon; Park, Donghwan; Paek, Jeongyeup; Ko, JeongGil

    2018-01-01

    With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.

  14. Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

    Science.gov (United States)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

    2017-01-01

    We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

  15. Morphological images analysis and chromosomic aberrations classification based on fuzzy logic

    International Nuclear Information System (INIS)

    Souza, Leonardo Peres

    2011-01-01

    This work has implemented a methodology for automation of images analysis of chromosomes of human cells irradiated at IEA-R1 nuclear reactor (located at IPEN, Sao Paulo, Brazil), and therefore subject to morphological aberrations. This methodology intends to be a tool for helping cytogeneticists on identification, characterization and classification of chromosomal metaphasic analysis. The methodology development has included the creation of a software application based on artificial intelligence techniques using Fuzzy Logic combined with image processing techniques. The developed application was named CHRIMAN and is composed of modules that contain the methodological steps which are important requirements in order to achieve an automated analysis. The first step is the standardization of the bi-dimensional digital image acquisition procedure through coupling a simple digital camera to the ocular of the conventional metaphasic analysis microscope. Second step is related to the image treatment achieved through digital filters application; storing and organization of information obtained both from image content itself, and from selected extracted features, for further use on pattern recognition algorithms. The third step consists on characterizing, counting and classification of stored digital images and extracted features information. The accuracy in the recognition of chromosome images is 93.9%. This classification is based on classical standards obtained at Buckton [1973], and enables support to geneticist on chromosomic analysis procedure, decreasing analysis time, and creating conditions to include this method on a broader evaluation system on human cell damage due to ionizing radiation exposure. (author)

  16. Comparison Effectiveness of Pixel Based Classification and Object Based Classification Using High Resolution Image In Floristic Composition Mapping (Study Case: Gunung Tidar Magelang City)

    Science.gov (United States)

    Ardha Aryaguna, Prama; Danoedoro, Projo

    2016-11-01

    Developments of analysis remote sensing have same way with development of technology especially in sensor and plane. Now, a lot of image have high spatial and radiometric resolution, that's why a lot information. Vegetation object analysis such floristic composition got a lot advantage of that development. Floristic composition can be interpreted using a lot of method such pixel based classification and object based classification. The problems for pixel based method on high spatial resolution image are salt and paper who appear in result of classification. The purpose of this research are compare effectiveness between pixel based classification and object based classification for composition vegetation mapping on high resolution image Worldview-2. The results show that pixel based classification using majority 5×5 kernel windows give the highest accuracy between another classifications. The highest accuracy is 73.32% from image Worldview-2 are being radiometric corrected level surface reflectance, but for overall accuracy in every class, object based are the best between another methods. Reviewed from effectiveness aspect, pixel based are more effective then object based for vegetation composition mapping in Tidar forest.

  17. Angular relational signature-based chest radiograph image view classification.

    Science.gov (United States)

    Santosh, K C; Wendling, Laurent

    2018-01-22

    In a computer-aided diagnosis (CAD) system, especially for chest radiograph or chest X-ray (CXR) screening, CXR image view information is required. Automatically separating CXR image view, frontal and lateral can ease subsequent CXR screening process, since the techniques may not equally work for both views. We present a novel technique to classify frontal and lateral CXR images, where we introduce angular relational signature through force histogram to extract features and apply three different state-of-the-art classifiers: multi-layer perceptron, random forest, and support vector machine to make a decision. We validated our fully automatic technique on a set of 8100 images hosted by the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH), and achieved an accuracy close to 100%. Our method outperforms the state-of-the-art methods in terms of processing time (less than or close to 2 s for the whole test data) while the accuracies can be compared, and therefore, it justifies its practicality. Graphical Abstract Interpreting chest X-ray (CXR) through the angular relational signature.

  18. Objective Color Classification of Ecstasy Tablets by Hyperspectral Imaging

    NARCIS (Netherlands)

    Edelman, Gerda; Lopatka, Martin; Aalders, Maurice

    2013-01-01

    The general procedure followed in the examination of ecstasy tablets for profiling purposes includes a color description, which depends highly on the observers' perception. This study aims to provide objective quantitative color information using visible hyperspectral imaging. Both self-manufactured

  19. Words Matter: Scene Text for Image Classification and Retrieval

    NARCIS (Netherlands)

    Karaoglu, S.; Tao, R.; Gevers, T.; Smeulders, A.W.M.

    Text in natural images typically adds meaning to an object or scene. In particular, text specifies which business places serve drinks (e.g., cafe, teahouse) or food (e.g., restaurant, pizzeria), and what kind of service is provided (e.g., massage, repair). The mere presence of text, its words, and

  20. Mass Spectrometry Imaging for the Classification of Tumor Tissue

    NARCIS (Netherlands)

    Mascini, N.E.

    2016-01-01

    Mass spectrometry imaging (MSI) can detect and identify many different molecules without the need for labeling. In addition, it can provide their spatial distributions as ‘molecular maps’. These features make MSI well suited for studying the molecular makeup of tumor tissue. Currently, there is an

  1. Image-Based Delineation and Classification of Built Heritage Masonry

    Directory of Open Access Journals (Sweden)

    Noelia Oses

    2014-02-01

    Full Text Available Fundación Zain is developing new built heritage assessment protocols. The goal is to objectivize and standardize the analysis and decision process that leads to determining the degree of protection of built heritage in the Basque Country. The ultimate step in this objectivization and standardization effort will be the development of an information and communication technology (ICT tool for the assessment of built heritage. This paper presents the ground work carried out to make this tool possible: the automatic, image-based delineation of stone masonry. This is a necessary first step in the development of the tool, as the built heritage that will be assessed consists of stone masonry construction, and many of the features analyzed can be characterized according to the geometry and arrangement of the stones. Much of the assessment is carried out through visual inspection. Thus, this process will be automated by applying image processing on digital images of the elements under inspection. The principal contribution of this paper is the automatic delineation the framework proposed. The other contribution is the performance evaluation of this delineation as the input to a classifier for a geometrically characterized feature of a built heritage object. The element chosen to perform this evaluation is the stone arrangement of masonry walls. The validity of the proposed framework is assessed on real images of masonry walls.

  2. Evolutionary image simplification for lung nodule classification with convolutional neural networks.

    Science.gov (United States)

    Lückehe, Daniel; von Voigt, Gabriele

    2018-05-29

    Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

  3. Robust through-the-wall radar image classification using a target-model alignment procedure.

    Science.gov (United States)

    Smith, Graeme E; Mobasseri, Bijan G

    2012-02-01

    A through-the-wall radar image (TWRI) bears little resemblance to the equivalent optical image, making it difficult to interpret. To maximize the intelligence that may be obtained, it is desirable to automate the classification of targets in the image to support human operators. This paper presents a technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs. The dependence of the image on the target location is discussed using a system point spread function (PSF) approach. It is shown that the position dependence will cause a classifier to fail, unless the image to be classified is aligned to a classifier-training location. A target image alignment technique based on deconvolution of the image with the system PSF is proposed. Comparison of the aligned target images with measured images shows the alignment process introducing normalized mean squared error (NMSE) ≤ 9%. The HRRP extracted from aligned target images are classified using a naive Bayesian classifier supported by principal component analysis. The classifier is tested using a real TWRI of canonical targets behind a concrete wall and shown to obtain correct classification rates ≥ 97%. © 2011 IEEE

  4. Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhigao Zeng

    2016-01-01

    Full Text Available This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.

  5. A novel underwater dam crack detection and classification approach based on sonar images.

    Science.gov (United States)

    Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Khan, Zubair; Li, Min

    2017-01-01

    Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments.

  6. A novel underwater dam crack detection and classification approach based on sonar images.

    Directory of Open Access Journals (Sweden)

    Pengfei Shi

    Full Text Available Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments.

  7. Classification of Diabetic Macular Edema and Its Stages Using Color Fundus Image

    Institute of Scientific and Technical Information of China (English)

    Muhammad Zubair; Shoab A. Khan; Ubaid Ullah Yasin

    2014-01-01

    Diabetic macular edema (DME) is a retinal thickening involving the center of the macula. It is one of the serious eye diseases which affects the central vision and can lead to partial or even complete visual loss. The only cure is timely diagnosis, prevention, and treatment of the disease. This paper presents an automated system for the diagnosis and classification of DME using color fundus image. In the proposed technique, first the optic disc is removed by applying some preprocessing steps. The preprocessed image is then passed through a classifier for segmentation of the image to detect exudates. The classifier uses dynamic thresholding technique by using some input parameters of the image. The stage classification is done on the basis of anearly treatment diabetic retinopathy study (ETDRS) given criteria to assess the severity of disease. The proposed technique gives a sensitivity, specificity, and accuracy of 98.27%, 96.58%, and 96.54%, respectively on publically available database.

  8. A software tool for automatic classification and segmentation of 2D/3D medical images

    International Nuclear Information System (INIS)

    Strzelecki, Michal; Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur

    2013-01-01

    Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided

  9. A software tool for automatic classification and segmentation of 2D/3D medical images

    Energy Technology Data Exchange (ETDEWEB)

    Strzelecki, Michal, E-mail: michal.strzelecki@p.lodz.pl [Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924 Lodz (Poland); Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur [Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924 Lodz (Poland)

    2013-02-21

    Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided.

  10. Classification of time-series images using deep convolutional neural networks

    Science.gov (United States)

    Hatami, Nima; Gavet, Yann; Debayle, Johan

    2018-04-01

    Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

  11. Deep multi-scale convolutional neural network for hyperspectral image classification

    Science.gov (United States)

    Zhang, Feng-zhe; Yang, Xia

    2018-04-01

    In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

  12. Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Haikel Alhichri

    2018-01-01

    Full Text Available This paper deals with the problem of the classification of large-scale very high-resolution (VHR remote sensing (RS images in a semisupervised scenario, where we have a limited training set (less than ten training samples per class. Typical pixel-based classification methods are unfeasible for large-scale VHR images. Thus, as a practical and efficient solution, we propose to subdivide the large image into a grid of tiles and then classify the tiles instead of classifying pixels. Our proposed method uses the power of a pretrained convolutional neural network (CNN to first extract descriptive features from each tile. Next, a neural network classifier (composed of 2 fully connected layers is trained in a semisupervised fashion and used to classify all remaining tiles in the image. This basically presents a coarse classification of the image, which is sufficient for many RS application. The second contribution deals with the employment of the semisupervised learning to improve the classification accuracy. We present a novel semisupervised approach which exploits both the spectral and spatial relationships embedded in the remaining unlabelled tiles. In particular, we embed a spectral graph Laplacian in the hidden layer of the neural network. In addition, we apply regularization of the output labels using a spatial graph Laplacian and the random Walker algorithm. Experimental results obtained by testing the method on two large-scale images acquired by the IKONOS2 sensor reveal promising capabilities of this method in terms of classification accuracy even with less than ten training samples per class.

  13. Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

    Directory of Open Access Journals (Sweden)

    Osmar Abílio de Carvalho Júnior

    2014-04-01

    Full Text Available Speckle noise (salt and pepper is inherent to synthetic aperture radar (SAR, which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA, a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS Phased-Array Synthetic-Aperture Radar (PALSAR sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondônia State (municipality of Candeias do Jamari, containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF or noise-adjusted principal components (NAPCs. Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.

  14. Classification of normal and abnormal images of lung cancer

    Science.gov (United States)

    Bhatnagar, Divyesh; Tiwari, Amit Kumar; Vijayarajan, V.; Krishnamoorthy, A.

    2017-11-01

    To find the exact symptoms of lung cancer is difficult, because of the formation of the most cancers tissues, wherein large structure of tissues is intersect in a different way. This problem can be evaluated with the help of digital images. In this strategy images will be examined with basic operation of PCA Algorithm. In this paper, GLCM method is used for pre-processing of the snap shots and function extraction system and to test the level of diseases of a patient in its premature stage get to know it is regular or unusual. With the help of result stage of cancer will be evaluated. With the help of dataset and result survival rate of cancer patient can be estimated. Result is based totally on the precise and wrong arrangement of the patterns of tissues.

  15. Compressive Sampling for Non-Imaging Remote Classification

    Science.gov (United States)

    2013-10-22

    spectro -­‐polarization  imager,   a  compressive  coherence  imager  to  resolve  objects  through  turbulence...2    The  relay  lens  for   UV -­‐CASSI,  which  focuses  the  aperture  code  onto  the  monochrome  detector...below  in  Fig.  3,  with  a  silicon   UV  sensitive  detector  on  the  left,  and   a   UV

  16. Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification

    DEFF Research Database (Denmark)

    Loosvelt, Lien; Peters, Jan; Skriver, Henning

    2012-01-01

    , we introduce Random Forests for the probabilistic mapping of vegetation from high-dimensional remote sensing data and present a comprehensive methodology to assess and analyze classification uncertainty based on the local probabilities of class membership. We apply this method to SAR image data...

  17. Comparative study of deep learning methods for one-shot image classification (abstract)

    NARCIS (Netherlands)

    van den Bogaert, J.; Mohseni, H.; Khodier, M.; Stoyanov, Y.; Mocanu, D.C.; Menkovski, V.

    2017-01-01

    Training deep learning models for images classification requires large amount of labeled data to overcome the challenges of overfitting and underfitting. Usually, in many practical applications, these labeled data are not available. In an attempt to solve this problem, the one-shot learning paradigm

  18. Internal representations for face detection: an application of noise-based image classification to BOLD responses.

    Science.gov (United States)

    Nestor, Adrian; Vettel, Jean M; Tarr, Michael J

    2013-11-01

    What basic visual structures underlie human face detection and how can we extract such structures directly from the amplitude of neural responses elicited by face processing? Here, we address these issues by investigating an extension of noise-based image classification to BOLD responses recorded in high-level visual areas. First, we assess the applicability of this classification method to such data and, second, we explore its results in connection with the neural processing of faces. To this end, we construct luminance templates from white noise fields based on the response of face-selective areas in the human ventral cortex. Using behaviorally and neurally-derived classification images, our results reveal a family of simple but robust image structures subserving face representation and detection. Thus, we confirm the role played by classical face selective regions in face detection and we help clarify the representational basis of this perceptual function. From a theory standpoint, our findings support the idea of simple but highly diagnostic neurally-coded features for face detection. At the same time, from a methodological perspective, our work demonstrates the ability of noise-based image classification in conjunction with fMRI to help uncover the structure of high-level perceptual representations. Copyright © 2012 Wiley Periodicals, Inc.

  19. Magnetic resonance imaging in classification of congenital muscular dystrophies with brain abnormalities

    NARCIS (Netherlands)

    vanderKnaap, MS; Smit, LME; Barth, PG; CatsmanBerrevoets, CE; Brouwer, OF; Begeer, JH; deCoo, IFM; Valk, J.

    A survey was performed of magnetic resonance imaging (MRI) findings in 21 patients with congenital muscular dystrophy (QID) with cerebral abnormalities to evaluate the contribution of MRI to the classification of CMD patients. In 5 patients with Walker-Warburg syndrome (WWS), MRI showed

  20. Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images.

    Science.gov (United States)

    Cheng, Phillip M; Malhi, Harshawn S

    2017-04-01

    The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized into 11 categories based on the text annotation specified by the technologist for the image. Cropped images were rescaled to 256 × 256 resolution and randomized, with 4094 images from 136 studies constituting the training set, and 1423 images from 49 studies constituting the test set. The fully connected layers of two convolutional neural networks based on CaffeNet and VGGNet, previously trained on the 2012 Large Scale Visual Recognition Challenge data set, were retrained on the training set. Weights in the convolutional layers of each network were frozen to serve as fixed feature extractors. Accuracy on the test set was evaluated for each network. A radiologist experienced in abdominal ultrasound also independently classified the images in the test set into the same 11 categories. The CaffeNet network classified 77.3% of the test set images accurately (1100/1423 images), with a top-2 accuracy of 90.4% (1287/1423 images). The larger VGGNet network classified 77.9% of the test set accurately (1109/1423 images), with a top-2 accuracy of VGGNet was 89.7% (1276/1423 images). The radiologist classified 71.7% of the test set images correctly (1020/1423 images). The differences in classification accuracies between both neural networks and the radiologist were statistically significant (p convolutional neural networks may be used to construct effective classifiers for abdominal ultrasound images.

  1. A MURI Center for Intelligent Biomimetic Image Processing and Classification

    Science.gov (United States)

    2007-11-01

    times, labeled "plage" or "open space" or "natural", the system learns to associate multiple classes with a given input. Testbed image examples have shown...brain color perception and category learning. Commentary on "Coordinating perceptually grounded categories through language " by Luc Steels and Tony...Symposium on Computational Intelligence (ISCI), Kosice, Slovakia, June 2002. 9. Carpenter, G.A., Award from the Slovak Artificial Intelligence Society, 2002

  2. Multifractal characterisation and classification of bread crumb digital images

    OpenAIRE

    Baravalle, Rodrigo Guillermo; Delrieux, Claudio Augusto; Gómez, Juan Carlos

    2017-01-01

    Adequate models of the bread crumb structure can be critical for understanding flow and transport processes in bread manufacturing, creating synthetic bread crumb images for photo-realistic rendering, evaluating similarities, and establishing quality features of different bread crumb types. In this article, multifractal analysis, employing the multifractal spectrum (MFS), has been applied to study the structure of the bread crumb in four varieties of bread (baguette, sliced, bran, and sandwic...

  3. Image Classification of Ribbed Smoked Sheet using Learning Vector Quantization

    Science.gov (United States)

    Rahmat, R. F.; Pulungan, A. F.; Faza, S.; Budiarto, R.

    2017-01-01

    Natural rubber is an important export commodity in Indonesia, which can be a major contributor to national economic development. One type of rubber used as rubber material exports is Ribbed Smoked Sheet (RSS). The quantity of RSS exports depends on the quality of RSS. RSS rubber quality has been assigned in SNI 06-001-1987 and the International Standards of Quality and Packing for Natural Rubber Grades (The Green Book). The determination of RSS quality is also known as the sorting process. In the rubber factones, the sorting process is still done manually by looking and detecting at the levels of air bubbles on the surface of the rubber sheet by naked eyes so that the result is subjective and not so good. Therefore, a method is required to classify RSS rubber automatically and precisely. We propose some image processing techniques for the pre-processing, zoning method for feature extraction and Learning Vector Quantization (LVQ) method for classifying RSS rubber into two grades, namely RSS1 and RSS3. We used 120 RSS images as training dataset and 60 RSS images as testing dataset. The result shows that our proposed method can give 89% of accuracy and the best perform epoch is in the fifteenth epoch.

  4. Comparison of Unsupervised Vegetation Classification Methods from Vhr Images after Shadows Removal by Innovative Algorithms

    Science.gov (United States)

    Movia, A.; Beinat, A.; Crosilla, F.

    2015-04-01

    The recognition of vegetation by the analysis of very high resolution (VHR) aerial images provides meaningful information about environmental features; nevertheless, VHR images frequently contain shadows that generate significant problems for the classification of the image components and for the extraction of the needed information. The aim of this research is to classify, from VHR aerial images, vegetation involved in the balance process of the environmental biochemical cycle, and to discriminate it with respect to urban and agricultural features. Three classification algorithms have been experimented in order to better recognize vegetation, and compared to NDVI index; unfortunately all these methods are conditioned by the presence of shadows on the images. Literature presents several algorithms to detect and remove shadows in the scene: most of them are based on the RGB to HSI transformations. In this work some of them have been implemented and compared with one based on RGB bands. Successively, in order to remove shadows and restore brightness on the images, some innovative algorithms, based on Procrustes theory, have been implemented and applied. Among these, we evaluate the capability of the so called "not-centered oblique Procrustes" and "anisotropic Procrustes" methods to efficiently restore brightness with respect to a linear correlation correction based on the Cholesky decomposition. Some experimental results obtained by different classification methods after shadows removal carried out with the innovative algorithms are presented and discussed.

  5. Mid-level image representations for real-time heart view plane classification of echocardiograms.

    Science.gov (United States)

    Penatti, Otávio A B; Werneck, Rafael de O; de Almeida, Waldir R; Stein, Bernardo V; Pazinato, Daniel V; Mendes Júnior, Pedro R; Torres, Ricardo da S; Rocha, Anderson

    2015-11-01

    In this paper, we explore mid-level image representations for real-time heart view plane classification of 2D echocardiogram ultrasound images. The proposed representations rely on bags of visual words, successfully used by the computer vision community in visual recognition problems. An important element of the proposed representations is the image sampling with large regions, drastically reducing the execution time of the image characterization procedure. Throughout an extensive set of experiments, we evaluate the proposed approach against different image descriptors for classifying four heart view planes. The results show that our approach is effective and efficient for the target problem, making it suitable for use in real-time setups. The proposed representations are also robust to different image transformations, e.g., downsampling, noise filtering, and different machine learning classifiers, keeping classification accuracy above 90%. Feature extraction can be performed in 30 fps or 60 fps in some cases. This paper also includes an in-depth review of the literature in the area of automatic echocardiogram view classification giving the reader a through comprehension of this field of study. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

    Directory of Open Access Journals (Sweden)

    Inhye Yoon

    2015-03-01

    Full Text Available Since incoming light to an unmanned aerial vehicle (UAV platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i image segmentation based on geometric classes; (ii generation of the context-adaptive transmission map; and (iii intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

  7. Wavelength-adaptive dehazing using histogram merging-based classification for UAV images.

    Science.gov (United States)

    Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

    2015-03-19

    Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

  8. Threshold selection for classification of MR brain images by clustering method

    Energy Technology Data Exchange (ETDEWEB)

    Moldovanu, Simona [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania); Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi (Romania); Obreja, Cristian; Moraru, Luminita, E-mail: luminita.moraru@ugal.ro [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania)

    2015-12-07

    Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

  9. Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features

    Science.gov (United States)

    Qin, Xulei; Lu, Guolan; Sechopoulos, Ioannis; Fei, Baowei

    2014-03-01

    Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

  10. Development of a Mobile Multispectral Imaging Platform for Precise Field Phenotyping

    Czech Academy of Sciences Publication Activity Database

    Svensgaard, J.; Roitsch, Thomas; Christensen, C.

    2014-01-01

    Roč. 4, č. 3 (2014), s. 322-336 ISSN 2073-4395 Institutional support: RVO:67179843 Keywords : field phenotyping * multispectral imaging * supervised classification * canonical discriminant analysis * vegetation coverage * NDVI Subject RIV: EH - Ecology, Behaviour

  11. Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL

    Science.gov (United States)

    Lazcano, R.; Madroñal, D.; Fabelo, H.; Ortega, S.; Salvador, R.; Callicó, G. M.; Juárez, E.; Sanz, C.

    2017-10-01

    Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVCCAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one-band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

  12. Classification of fecal contamination on leafy greens by hyperspectral imaging

    Science.gov (United States)

    Yang, Chun-Chieh; Jun, Won; Kim, Moon S.; Chao, Kaunglin; Kang, Sukwon; Chan, Diane E.; Lefcourt, Alan

    2010-04-01

    This paper reported the development of hyperspectral fluorescence imaging system using ultraviolet-A excitation (320-400 nm) for detection of bovine fecal contaminants on the abaxial and adaxial surfaces of romaine lettuce and baby spinach leaves. Six spots of fecal contamination were applied to each of 40 lettuce and 40 spinach leaves. In this study, the wavebands at 666 nm and 680 nm were selected by the correlation analysis. The two-band ratio, 666 nm / 680 nm, of fluorescence intensity was used to differentiate the contaminated spots from uncontaminated leaf area. The proposed method could accurately detect all of the contaminated spots.

  13. Classification and Quality Evaluation of Tobacco Leaves Based on Image Processing and Fuzzy Comprehensive Evaluation

    Science.gov (United States)

    Zhang, Fan; Zhang, Xinhong

    2011-01-01

    Most of classification, quality evaluation or grading of the flue-cured tobacco leaves are manually operated, which relies on the judgmental experience of experts, and inevitably limited by personal, physical and environmental factors. The classification and the quality evaluation are therefore subjective and experientially based. In this paper, an automatic classification method of tobacco leaves based on the digital image processing and the fuzzy sets theory is presented. A grading system based on image processing techniques was developed for automatically inspecting and grading flue-cured tobacco leaves. This system uses machine vision for the extraction and analysis of color, size, shape and surface texture. Fuzzy comprehensive evaluation provides a high level of confidence in decision making based on the fuzzy logic. The neural network is used to estimate and forecast the membership function of the features of tobacco leaves in the fuzzy sets. The experimental results of the two-level fuzzy comprehensive evaluation (FCE) show that the accuracy rate of classification is about 94% for the trained tobacco leaves, and the accuracy rate of the non-trained tobacco leaves is about 72%. We believe that the fuzzy comprehensive evaluation is a viable way for the automatic classification and quality evaluation of the tobacco leaves. PMID:22163744

  14. A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

    Directory of Open Access Journals (Sweden)

    Huai Yu

    2016-03-01

    Full Text Available Scene classification plays an important role in understanding high-resolution satellite (HRS remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT, which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms.

  15. Reconstruction of hyperspectral image using matting model for classification

    Science.gov (United States)

    Xie, Weiying; Li, Yunsong; Ge, Chiru

    2016-05-01

    Although hyperspectral images (HSIs) captured by satellites provide much information in spectral regions, some bands are redundant or have large amounts of noise, which are not suitable for image analysis. To address this problem, we introduce a method for reconstructing the HSI with noise reduction and contrast enhancement using a matting model for the first time. The matting model refers to each spectral band of an HSI that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, one spectral band of an HSI with more refined information than most other bands is selected, and is referred to as an alpha channel of the HSI to estimate the hyperspectral foreground and hyperspectral background. Finally, a combination operation is applied to reconstruct the HSI. In addition, the support vector machine (SVM) classifier and three sparsity-based classifiers, i.e., orthogonal matching pursuit (OMP), simultaneous OMP, and OMP based on first-order neighborhood system weighted classifiers, are utilized on the reconstructed HSI and the original HSI to verify the effectiveness of the proposed method. Specifically, using the reconstructed HSI, the average accuracy of the SVM classifier can be improved by as much as 19%.

  16. Multispectral image analysis for object recognition and classification

    Science.gov (United States)

    Viau, C. R.; Payeur, P.; Cretu, A.-M.

    2016-05-01

    Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate decision-making processes. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various fields including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance. The fundamental objective of this research project was to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train supp