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....

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

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.

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...

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

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.

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.

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

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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.

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

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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.

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.

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.

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.

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.

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.

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, ...

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

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.

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.

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.

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.

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

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.

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.

Observation versus classification in supervised category learning.

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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.

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

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.

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.

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.

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

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%.

A supervised learning rule for classification of spatiotemporal spike patterns.

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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.

Robust Semi-Supervised Manifold Learning Algorithm for Classification

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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.

Supervised Learning for Visual Pattern Classification

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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.

Semi-supervised morphosyntactic classification of Old Icelandic.

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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.

An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

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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.

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

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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....

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

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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.

Supervised classification of continental shelf sediment off western Donegal, Ireland

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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.

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

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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.

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

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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…

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

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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.

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

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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.

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

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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.

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

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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).

Remote Sensing Image Classification Based on Stacked Denoising Autoencoder

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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.

Constrained Deep Weak Supervision for Histopathology Image Segmentation.

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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.

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

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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.

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

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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.

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

Musical Instrument Classification Based on Nonlinear Recurrence Analysis and Supervised Learning

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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.

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

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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…

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

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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.

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

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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.

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

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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.

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...

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

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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

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

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

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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.

Supervised and Unsupervised Classification for Pattern Recognition Purposes

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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

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...

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.

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.

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.

[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.

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.

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.

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

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

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.

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

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.

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.

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.

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.

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.

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...

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

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.)

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

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

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 ...

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...

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...

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 ...

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.

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

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.

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.

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.

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.

Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules.

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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.

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

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.

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...

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.

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

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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.

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

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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.

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.

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...

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.

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.

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.

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...

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.

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.

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

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

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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.

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

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.

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.

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

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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.

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.

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.

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

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...

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.

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

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.

Supervised Filter Learning for Representation Based Face Recognition.

Directory of Open Access Journals (Sweden)

Chao Bi

Full Text Available Representation based classification methods, such as Sparse Representation Classification (SRC and Linear Regression Classification (LRC have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm.

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.

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.

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.

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

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.

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.

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

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....

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.

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).

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 ...

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.

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.

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.

Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions.

Science.gov (United States)

Chen, Ke; Wang, Shihai

2011-01-01

Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stagewise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real-world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms, including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.

SemiBoost: boosting for semi-supervised learning.

Science.gov (United States)

Mallapragada, Pavan Kumar; Jin, Rong; Jain, Anil K; Liu, Yi

2009-11-01

Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by using the available unlabeled examples. We call this as the Semi-supervised improvement problem, to distinguish the proposed approach from the existing approaches. We design a metasemi-supervised learning algorithm that wraps around the underlying supervised algorithm and improves its performance using unlabeled data. This problem is particularly important when we need to train a supervised learning algorithm with a limited number of labeled examples and a multitude of unlabeled examples. We present a boosting framework for semi-supervised learning, termed as SemiBoost. The key advantages of the proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models. An empirical study on 16 different data sets and text categorization demonstrates that the proposed framework improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples. We also show that the performance of the proposed algorithm, SemiBoost, is comparable to the state-of-the-art semi-supervised learning algorithms.

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.

CLASSIFICATION BY USING MULTISPECTRAL POINT CLOUD DATA

Directory of Open Access Journals (Sweden)

C. T. Liao

2012-07-01

Full Text Available Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

Classification by Using Multispectral Point Cloud Data

Science.gov (United States)

Liao, C. T.; Huang, H. H.

2012-07-01

Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

Stochastic microstructure characterization and reconstruction via supervised learning

International Nuclear Information System (INIS)

Bostanabad, Ramin; Bui, Anh Tuan; Xie, Wei; Apley, Daniel W.; Chen, Wei

2016-01-01

Microstructure characterization and reconstruction have become indispensable parts of computational materials science. The main contribution of this paper is to introduce a general methodology for practical and efficient characterization and reconstruction of stochastic microstructures based on supervised learning. The methodology is general in that it can be applied to a broad range of microstructures (clustered, porous, and anisotropic). By treating the digitized microstructure image as a set of training data, we generically learn the stochastic nature of the microstructure via fitting a supervised learning model to it (we focus on classification trees). The fitted supervised learning model provides an implicit characterization of the joint distribution of the collection of pixel phases in the image. Based on this characterization, we propose two different approaches to efficiently reconstruct any number of statistically equivalent microstructure samples. We test the approach on five examples and show that the spatial dependencies within the microstructures are well preserved, as evaluated via correlation and lineal-path functions. The main advantages of our approach stem from having a compact empirically-learned model that characterizes the stochastic nature of the microstructure, which not only makes reconstruction more computationally efficient than existing methods, but also provides insight into morphological complexity.

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.

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.

Supervised versus unsupervised categorization: two sides of the same coin?

Science.gov (United States)

Pothos, Emmanuel M; Edwards, Darren J; Perlman, Amotz

2011-09-01

Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. ( 2011 ; Pothos et al., 2008 ) with the results from two procedures of supervised categorization. In two experiments, we tested 375 participants with nine different stimulus sets and examined the relation between ease of learning of a classification, memory for a classification, and spontaneous preference for a classification. After taking into account the role of the number of category labels (clusters) in supervised learning, we found the three variables to be closely associated with each other. Our results provide encouragement for researchers seeking unified theoretical explanations for supervised and unsupervised categorization, but raise a range of challenging theoretical questions.

Training strategy for convolutional neural networks in pedestrian gender classification

Science.gov (United States)

Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

2017-06-01

In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

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.

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.

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

Science.gov (United States)

Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

2016-12-01

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

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.

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.

Classification of Polarimetric SAR Data Using Dictionary Learning

DEFF Research Database (Denmark)

Vestergaard, Jacob Schack; Nielsen, Allan Aasbjerg; Dahl, Anders Lindbjerg

2012-01-01

This contribution deals with classification of multilook fully polarimetric synthetic aperture radar (SAR) data by learning a dictionary of crop types present in the Foulum test site. The Foulum test site contains a large number of agricultural fields, as well as lakes, forests, natural vegetation......, grasslands and urban areas, which make it ideally suited for evaluation of classification algorithms. Dictionary learning centers around building a collection of image patches typical for the classification problem at hand. This requires initial manual labeling of the classes present in the data and is thus...... a method for supervised classification. Sparse coding of these image patches aims to maintain a proficient number of typical patches and associated labels. Data is consecutively classified by a nearest neighbor search of the dictionary elements and labeled with probabilities of each class. Each dictionary...

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Directory of Open Access Journals (Sweden)

Christoff Fourie

2014-11-01

Full Text Available Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA. Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

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

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.

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.

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.

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.

Semi-supervised and unsupervised extreme learning machines.

Science.gov (United States)

Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

2014-12-01

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

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.

Comparing and optimizing land use classification in a Himalayan area using parametric and non parametric approaches

NARCIS (Netherlands)

Sterk, G.; Sameer Saran,; Raju, P.L.N.; Amit, Bharti

2007-01-01

Supervised classification is one of important tasks in remote sensing image interpretation, in which the image pixels are classified to various predefined land use/land cover classes based on the spectral reflectance values in different bands. In reality some classes may have very close spectral

A new supervised learning algorithm for spiking neurons.

Science.gov (United States)

Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

2013-06-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

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

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

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...

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

Pulsed terahertz imaging of breast cancer in freshly excised murine tumors

Science.gov (United States)

Bowman, Tyler; Chavez, Tanny; Khan, Kamrul; Wu, Jingxian; Chakraborty, Avishek; Rajaram, Narasimhan; Bailey, Keith; El-Shenawee, Magda

2018-02-01

This paper investigates terahertz (THz) imaging and classification of freshly excised murine xenograft breast cancer tumors. These tumors are grown via injection of E0771 breast adenocarcinoma cells into the flank of mice maintained on high-fat diet. Within 1 h of excision, the tumor and adjacent tissues are imaged using a pulsed THz system in the reflection mode. The THz images are classified using a statistical Bayesian mixture model with unsupervised and supervised approaches. Correlation with digitized pathology images is conducted using classification images assigned by a modal class decision rule. The corresponding receiver operating characteristic curves are obtained based on the classification results. A total of 13 tumor samples obtained from 9 tumors are investigated. The results show good correlation of THz images with pathology results in all samples of cancer and fat tissues. For tumor samples of cancer, fat, and muscle tissues, THz images show reasonable correlation with pathology where the primary challenge lies in the overlapping dielectric properties of cancer and muscle tissues. The use of a supervised regression approach shows improvement in the classification images although not consistently in all tissue regions. Advancing THz imaging of breast tumors from mice and the development of accurate statistical models will ultimately progress the technique for the assessment of human breast tumor margins.

Image-based reflectance conversion of ASTER and IKONOS ...

African Journals Online (AJOL)

Spectral signatures derived from different image-based models for ASTER and IKONOS were inspected visually as first departure. This was followed by comparison of the total accuracy and Kappa index computed from supervised classification of images that were derived from different image-based atmospheric correction ...

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.

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.

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.

A comparison of Landsat point and rectangular field training sets for land-use classification

Science.gov (United States)

Tom, C. H.; Miller, L. D.

1984-01-01

Rectangular training fields of homogeneous spectroreflectance are commonly used in supervised pattern recognition efforts. Trial image classification with manually selected training sets gives irregular and misleading results due to statistical bias. A self-verifying, grid-sampled training point approach is proposed as a more statistically valid feature extraction technique. A systematic pixel sampling network of every ninth row and ninth column efficiently replaced the full image scene with smaller statistical vectors which preserved the necessary characteristics for classification. The composite second- and third-order average classification accuracy of 50.1 percent for 331,776 pixels in the full image substantially agreed with the 51 percent value predicted by the grid-sampled, 4,100-point training set.

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...

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.

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.

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

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning.

Science.gov (United States)

Onder, Devrim; Sarioglu, Sulen; Karacali, Bilge

2013-04-01

classifier, suggest that quasi-supervised image texture labelling may be a useful method in the analysis and classification of pathological slides but further study is required to improve the results. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

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...

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

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

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

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

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.

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.

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.

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.

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.

Classification of multiple sclerosis lesions using adaptive dictionary learning.

Science.gov (United States)

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Active semi-supervised learning method with hybrid deep belief networks.

Science.gov (United States)

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

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.

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)

Automated classification of cell morphology by coherence-controlled holographic microscopy

Science.gov (United States)

Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

2017-08-01

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

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.

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....

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.

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.

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.

Predicting protein complexes using a supervised learning method combined with local structural information.

Science.gov (United States)

Dong, Yadong; Sun, Yongqi; Qin, Chao

2018-01-01

The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

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...

Assessment of various supervised learning algorithms using different performance metrics

Science.gov (United States)

Susheel Kumar, S. M.; Laxkar, Deepak; Adhikari, Sourav; Vijayarajan, V.

2017-11-01

Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.

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.

Image processing developments and applications for water quality monitoring and trophic state determination

International Nuclear Information System (INIS)

Blackwell, R.J.

1982-03-01

Remote sensing data analysis of water quality monitoring is evaluated. Data anaysis and image processing techniques are applied to LANDSAT remote sensing data to produce an effective operational tool for lake water quality surveying and monitoring. Digital image processing and analysis techniques were designed, developed, tested, and applied to LANDSAT multispectral scanner (MSS) data and conventional surface acquired data. Utilization of these techniques facilitates the surveying and monitoring of large numbers of lakes in an operational manner. Supervised multispectral classification, when used in conjunction with surface acquired water quality indicators, is used to characterize water body trophic status. Unsupervised multispectral classification, when interpreted by lake scientists familiar with a specific water body, yields classifications of equal validity with supervised methods and in a more cost effective manner. Image data base technology is used to great advantage in characterizing other contributing effects to water quality. These effects include drainage basin configuration, terrain slope, soil, precipitation and land cover characteristics

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.

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

Sow-activity classification from acceleration patterns

DEFF Research Database (Denmark)

Escalante, Hugo Jair; Rodriguez, Sara V.; Cordero, Jorge

2013-01-01

sow-activity classification can be approached with standard machine learning methods for pattern classification. Individual predictions for elements of times series of arbitrary length are combined to classify it as a whole. An extensive comparison of representative learning algorithms, including......This paper describes a supervised learning approach to sow-activity classification from accelerometer measurements. In the proposed methodology, pairs of accelerometer measurements and activity types are considered as labeled instances of a usual supervised classification task. Under this scenario...... neural networks, support vector machines, and ensemble methods, is presented. Experimental results are reported using a data set for sow-activity classification collected in a real production herd. The data set, which has been widely used in related works, includes measurements from active (Feeding...

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.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

Science.gov (United States)

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

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.

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...

Global Optimization Ensemble Model for Classification Methods

Science.gov (United States)

Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab

2014-01-01

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382

Global Optimization Ensemble Model for Classification Methods

Directory of Open Access Journals (Sweden)

Hina Anwar

2014-01-01

Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.

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....

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.

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.

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

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

Science.gov (United States)

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

Automated classification of cell morphology by coherence-controlled holographic microscopy.

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Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

2017-08-01

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

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.

Fatigue Level Estimation of Bill Based on Acoustic Signal Feature by Supervised SOM

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Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

Fatigued bills have harmful influence on daily operation of Automated Teller Machine(ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. We propose a new method to estimate bending rigidity of bill from acoustic signal feature of banking machines. The estimated bending rigidities are used as continuous fatigue level for classification of fatigued bill. By using the supervised Self-Organizing Map(supervised SOM), we estimate the bending rigidity from only the acoustic energy pattern effectively. The experimental result with real bill samples shows the effectiveness of the proposed method.

Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

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Nan Zhao

2014-05-01

Full Text Available Single nucleotide polymorphisms (SNPs are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs have been found near or inside the protein-protein interaction (PPI interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor. Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1 a 2-class problem (strengthening/weakening PPI mutations, (2 another 2-class problem (mutations that disrupt/preserve a PPI, and (3 a 3-class classification (detrimental/neutral/beneficial mutation effects. In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the

Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

Science.gov (United States)

Zhao, Nan; Han, Jing Ginger; Shyu, Chi-Ren; Korkin, Dmitry

2014-01-01

Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of

Radiomic features analysis in computed tomography images of lung nodule classification.

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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.

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

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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.

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.

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 %.

MULTI-TEMPORAL CLASSIFICATION AND CHANGE DETECTION USING UAV IMAGES

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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.

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

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.

Peripheral blood smear image analysis: A comprehensive review

Directory of Open Access Journals (Sweden)

Emad A Mohammed

2014-01-01

Full Text Available Peripheral blood smear image examination is a part of the routine work of every laboratory. The manual examination of these images is tedious, time-consuming and suffers from interobserver variation. This has motivated researchers to develop different algorithms and methods to automate peripheral blood smear image analysis. Image analysis itself consists of a sequence of steps consisting of image segmentation, features extraction and selection and pattern classification. The image segmentation step addresses the problem of extraction of the object or region of interest from the complicated peripheral blood smear image. Support vector machine (SVM and artificial neural networks (ANNs are two common approaches to image segmentation. Features extraction and selection aims to derive descriptive characteristics of the extracted object, which are similar within the same object class and different between different objects. This will facilitate the last step of the image analysis process: pattern classification. The goal of pattern classification is to assign a class to the selected features from a group of known classes. There are two types of classifier learning algorithms: supervised and unsupervised. Supervised learning algorithms predict the class of the object under test using training data of known classes. The training data have a predefined label for every class and the learning algorithm can utilize this data to predict the class of a test object. Unsupervised learning algorithms use unlabeled training data and divide them into groups using similarity measurements. Unsupervised learning algorithms predict the group to which a new test object belong to, based on the training data without giving an explicit class to that object. ANN, SVM, decision tree and K-nearest neighbor are possible approaches to classification algorithms. Increased discrimination may be obtained by combining several classifiers together.

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.

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

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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.

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.

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.

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

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.

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

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

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

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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

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

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.

Classification of astrocyto-mas and meningiomas using statistical discriminant analysis on MRI data

International Nuclear Information System (INIS)

Siromoney, Anna; Prasad, G.N.S.; Raghuram, Lakshminarayan; Korah, Ipeson; Siromoney, Arul; Chandrasekaran, R.

2001-01-01

The objective of this study was to investigate the usefulness of Multivariate Discriminant Analysis for classifying two groups of primary brain tumours, astrocytomas and meningiomas, from Magnetic Resonance Images. Discriminant analysis is a multivariate technique concerned with separating distinct sets of objects and with allocating new objects to previously defined groups. Allocation or classification rules are usually developed from learning examples in a supervised learning environment. Data from signal intensity measurements in the multiple scan performed on each patient in routine clinical scanning was analysed using Fisher's Classification, which is one method of discriminant analysis

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

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 .

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.

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

Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning

Science.gov (United States)

Leavesley, Silas J.; Deal, Joshua; Hill, Shante; Martin, Will A.; Lall, Malvika; Lopez, Carmen; Rider, Paul F.; Rich, Thomas C.; Boudreaux, Carole W.

2018-02-01

Hyperspectral imaging technologies have shown great promise for biomedical applications. These techniques have been especially useful for detection of molecular events and characterization of cell, tissue, and biomaterial composition. Unfortunately, hyperspectral imaging technologies have been slow to translate to clinical devices - likely due to increased cost and complexity of the technology as well as long acquisition times often required to sample a spectral image. We have demonstrated that hyperspectral imaging approaches which scan the fluorescence excitation spectrum can provide increased signal strength and faster imaging, compared to traditional emission-scanning approaches. We have also demonstrated that excitation-scanning approaches may be able to detect spectral differences between colonic adenomas and adenocarcinomas and normal mucosa in flash-frozen tissues. Here, we report feasibility results from using excitation-scanning hyperspectral imaging to screen pairs of fresh tumoral and nontumoral colorectal tissues. Tissues were imaged using a novel hyperspectral imaging fluorescence excitation scanning microscope, sampling a wavelength range of 360-550 nm, at 5 nm increments. Image data were corrected to achieve a NIST-traceable flat spectral response. Image data were then analyzed using a range of supervised and unsupervised classification approaches within ENVI software (Harris Geospatial Solutions). Supervised classification resulted in >99% accuracy for single-patient image data, but only 64% accuracy for multi-patient classification (n=9 to date), with the drop in accuracy due to increased false-positive detection rates. Hence, initial data indicate that this approach may be a viable detection approach, but that larger patient sample sizes need to be evaluated and the effects of inter-patient variability studied.

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.

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.

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

Support Vector Machine Based Tool for Plant Species Taxonomic Classification

OpenAIRE

Manimekalai .K; Vijaya.MS

2014-01-01

Plant species are living things and are generally categorized in terms of Domain, Kingdom, Phylum, Class, Order, Family, Genus and name of Species in a hierarchical fashion. This paper formulates the taxonomic leaf categorization problem as the hierarchical classification task and provides a suitable solution using a supervised learning technique namely support vector machine. Features are extracted from scanned images of plant leaves and trained using SVM. Only class, order, family of plants...

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.

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...

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.

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

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

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.

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.

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.

Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.

Science.gov (United States)

Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda

2017-08-20

Alzheimer's disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer's and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93

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.

Latent class models for classification

NARCIS (Netherlands)

Vermunt, J.K.; Magidson, J.

2003-01-01

An overview is provided of recent developments in the use of latent class (LC) and other types of finite mixture models for classification purposes. Several extensions of existing models are presented. Two basic types of LC models for classification are defined: supervised and unsupervised

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....

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.

Empirical Studies On Machine Learning Based Text Classification Algorithms

OpenAIRE

Shweta C. Dharmadhikari; Maya Ingle; Parag Kulkarni

2011-01-01

Automatic classification of text documents has become an important research issue now days. Properclassification of text documents requires information retrieval, machine learning and Natural languageprocessing (NLP) techniques. Our aim is to focus on important approaches to automatic textclassification based on machine learning techniques viz. supervised, unsupervised and semi supervised.In this paper we present a review of various text classification approaches under machine learningparadig...

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.

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.

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,...

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.

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

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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.

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.

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.

Gender classification system in uncontrolled environments

Science.gov (United States)

Zeng, Pingping; Zhang, Yu-Jin; Duan, Fei

2011-01-01

Most face analysis systems available today perform mainly on restricted databases of images in terms of size, age, illumination. In addition, it is frequently assumed that all images are frontal and unconcealed. Actually, in a non-guided real-time supervision, the face pictures taken may often be partially covered and with head rotation less or more. In this paper, a special system supposed to be used in real-time surveillance with un-calibrated camera and non-guided photography is described. It mainly consists of five parts: face detection, non-face filtering, best-angle face selection, texture normalization, and gender classification. Emphases are focused on non-face filtering and best-angle face selection parts as well as texture normalization. Best-angle faces are figured out by PCA reconstruction, which equals to an implicit face alignment and results in a huge increase of the accuracy for gender classification. Dynamic skin model and a masked PCA reconstruction algorithm are applied to filter out faces detected in error. In order to fully include facial-texture and shape-outline features, a hybrid feature that is a combination of Gabor wavelet and PHoG (pyramid histogram of gradients) was proposed to equitable inner texture and outer contour. Comparative study on the effects of different non-face filtering and texture masking methods in the context of gender classification by SVM is reported through experiments on a set of UT (a company name) face images, a large number of internet images and CAS (Chinese Academy of Sciences) face database. Some encouraging results are obtained.

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification

Directory of Open Access Journals (Sweden)

Lu Bing

2017-01-01

Full Text Available 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.

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.

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.

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.

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...

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.

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

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

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.

CLASSIFICATION ALGORITHMS FOR BIG DATA ANALYSIS, A MAP REDUCE APPROACH

Directory of Open Access Journals (Sweden)

V. A. Ayma

2015-03-01

Full Text Available Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP, which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA’s machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines (SVM. The results of an experimental analysis using a SVM classifier on data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.

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.

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

Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

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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.

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

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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.

Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning

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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.

Classification of brain compartments and head injury lesions by neural networks applied to MRI

International Nuclear Information System (INIS)

Kischell, E.R.; Kehtarnavaz, N.; Hillman, G.R.; Levin, H.; Lilly, M.; Kent, T.A.

1995-01-01

An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and 'unknown'. A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician's report used to train the neural network. (orig.)

Classification of brain compartments and head injury lesions by neural networks applied to MRI

Energy Technology Data Exchange (ETDEWEB)

Kischell, E R [Dept. of Electrical Engineering, Texas A and M Univ., College Station, TX (United States); Kehtarnavaz, N [Dept. of Electrical Engineering, Texas A and M Univ., College Station, TX (United States); Hillman, G R [Dept. of Pharmacology, Univ. of Texas Medical Branch, Galveston, TX (United States); Levin, H [Dept. of Neurosurgery, Univ. of Texas Medical Branch, Galveston, TX (United States); Lilly, M [Dept. of Neurosurgery, Univ. of Texas Medical Branch, Galveston, TX (United States); Kent, T A [Dept. of Neurology and Psychiatry, Univ. of Texas Medical Branch, Galveston, TX (United States)

1995-10-01

An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and `unknown`. A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician`s report used to train the neural network. (orig.)

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...

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

Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

Directory of Open Access Journals (Sweden)

J. Del Rio Vera

2009-01-01

Full Text Available This paper presents a new supervised classification approach for automated target recognition (ATR in SAS images. The recognition procedure starts with a novel segmentation stage based on the Hilbert transform. A number of geometrical features are then extracted and used to classify observed objects against a previously compiled database of target and non-target features. The proposed approach has been tested on a set of 1528 simulated images created by the NURC SIGMAS sonar model, achieving up to 95% classification accuracy.

7 CFR 27.16 - Inspection; weighing; samples; supervision.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Inspection; weighing; samples; supervision. 27.16 Section 27.16 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...

A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

Directory of Open Access Journals (Sweden)

Dong Jiang

Full Text Available Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1 images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization with convenience.

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.

Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images

Directory of Open Access Journals (Sweden)

Fenghua Huang

2014-01-01

Full Text Available To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1 different radial basis kernel functions (RBFs are employed for spectral and textural features, and a new combined radial basis kernel function (CRBF is proposed by combining them in a weighted manner; (2 the binary decision tree-based multiclass SMO (BDT-SMO is used in the classification of hyperspectral fused images; (3 experiments are carried out, where the single radial basis function- (SRBF- based BDT-SMO classifier and the CRBF-based BDT-SMO classifier are used, respectively, to classify the land usages of hyperspectral fused images, and genetic algorithms (GA are used to optimize the kernel parameters of the classifiers. The results show that, compared with SRBF, CRBF-based BDT-SMO classifiers display greater classification accuracy and efficiency.

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.

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.

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

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.

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.

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

Cellular automata rule characterization and classification using texture descriptors

Science.gov (United States)

Machicao, Jeaneth; Ribas, Lucas C.; Scabini, Leonardo F. S.; Bruno, Odermir M.

2018-05-01

The cellular automata (CA) spatio-temporal patterns have attracted the attention from many researchers since it can provide emergent behavior resulting from the dynamics of each individual cell. In this manuscript, we propose an approach of texture image analysis to characterize and classify CA rules. The proposed method converts the CA spatio-temporal patterns into a gray-scale image. The gray-scale is obtained by creating a binary number based on the 8-connected neighborhood of each dot of the CA spatio-temporal pattern. We demonstrate that this technique enhances the CA rule characterization and allow to use different texture image analysis algorithms. Thus, various texture descriptors were evaluated in a supervised training approach aiming to characterize the CA's global evolution. Our results show the efficiency of the proposed method for the classification of the elementary CA (ECAs), reaching a maximum of 99.57% of accuracy rate according to the Li-Packard scheme (6 classes) and 94.36% for the classification of the 88 rules scheme. Moreover, within the image analysis context, we found a better performance of the method by means of a transformation of the binary states to a gray-scale.

Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification.

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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.

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.

REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA

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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.

Hyperspectral Image Classification Using Kernel Fukunaga-Koontz Transform

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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.

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

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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.

Interactive classification and content-based retrieval of tissue images

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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.

APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF HIGH RESOLUTION AGRICULTURAL REMOTE SENSING IMAGES

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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.

Automated retinal vessel type classification in color fundus images

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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.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

Science.gov (United States)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

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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.

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...

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.

Arabic Supervised Learning Method Using N-Gram

Science.gov (United States)

Sanan, Majed; Rammal, Mahmoud; Zreik, Khaldoun

2008-01-01

Purpose: Recently, classification of Arabic documents is a real problem for juridical centers. In this case, some of the Lebanese official journal documents are classified, and the center has to classify new documents based on these documents. This paper aims to study and explain the useful application of supervised learning method on Arabic texts…

How automated image analysis techniques help scientists in species identification and classification?

Science.gov (United States)

Yousef Kalafi, Elham; Town, Christopher; Kaur Dhillon, Sarinder

2017-09-04

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification increased over the last two decades. Automation of data classification is primarily focussed on images, incorporating and analysing image data has recently become easier due to developments in computational technology. Research efforts in identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, categorizing and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies.

Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery

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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.

A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

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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.

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...

Combining low level features and visual attributes for VHR remote sensing image classification

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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.

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

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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.

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

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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.

Image fusion using sparse overcomplete feature dictionaries

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Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

2015-10-06

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

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

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

Safe semi-supervised learning based on weighted likelihood.

Science.gov (United States)

Kawakita, Masanori; Takeuchi, Jun'ichi

2014-05-01

We are interested in developing a safe semi-supervised learning that works in any situation. Semi-supervised learning postulates that n(') unlabeled data are available in addition to n labeled data. However, almost all of the previous semi-supervised methods require additional assumptions (not only unlabeled data) to make improvements on supervised learning. If such assumptions are not met, then the methods possibly perform worse than supervised learning. Sokolovska, Cappé, and Yvon (2008) proposed a semi-supervised method based on a weighted likelihood approach. They proved that this method asymptotically never performs worse than supervised learning (i.e., it is safe) without any assumption. Their method is attractive because it is easy to implement and is potentially general. Moreover, it is deeply related to a certain statistical paradox. However, the method of Sokolovska et al. (2008) assumes a very limited situation, i.e., classification, discrete covariates, n(')→∞ and a maximum likelihood estimator. In this paper, we extend their method by modifying the weight. We prove that our proposal is safe in a significantly wide range of situations as long as n≤n('). Further, we give a geometrical interpretation of the proof of safety through the relationship with the above-mentioned statistical paradox. Finally, we show that the above proposal is asymptotically safe even when n(')

Color-Image Classification Using MRFs for an Outdoor Mobile Robot

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Moises Alencastre-Miranda

2005-02-01

Full Text Available In this paper, we suggest to use color-image classification (in several phases using Markov Random Fields (MRFs in order to understand natural images from outdoor environment's scenes for a mobile robot. We skip preprocessing phase having same results and better performance. In segmentation phase, we implement a color segmentation method considering I3 color space measure average in little image's cells obtained from a single split step. In classification phase, a MRF was used to identify regions as one of three selected classes; here, we consider at the same time the intrinsic color features of the image and the neighborhood system between image's cells. Finally, we use region growing and contextual information to correct misclassification errors. We have implemented and tested those phases with several images taken at our campus' gardens. We include some results in off-line processing mode and in on-line execution mode on an outdoor mobile robot. The vision system has been used for reactive exploration in an outdoor environment.

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

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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.

Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed.

Science.gov (United States)

Makkeasorn, Ammarin; Chang, Ni-Bin; Li, Jiahong

2009-02-01

Riparian zones are deemed significant due to their interception capability of non-point source impacts and the maintenance of ecosystem integrity region wide. To improve classification and change detection of riparian buffers, this paper developed an evolutionary computational, supervised classification method--the RIparian Classification Algorithm (RICAL)--to conduct the seasonal change detection of riparian zones in a vast semi-arid watershed, South Texas. RICAL uniquely demonstrates an integrative effort to incorporate both vegetation indices and soil moisture images derived from LANDSAT 5 TM and RADARSAT-1 satellite images, respectively. First, an estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) images was conducted via the first-stage genetic programming (GP) practice. Second, for the statistical analyses and image classification, eight vegetation indices were prepared based on reflectance factors that were calculated as the response of the instrument on LANDSAT. These spectral vegetation indices were then independently used for discriminate analysis along with soil moisture images to classify the riparian zones via the second-stage GP practice. The practical implementation was assessed by a case study in the Choke Canyon Reservoir Watershed (CCRW), South Texas, which is mostly agricultural and range land in a semi-arid coastal environment. To enhance the application potential, a combination of Iterative Self-Organizing Data Analysis Techniques (ISODATA) and maximum likelihood supervised classification was also performed for spectral discrimination and classification of riparian varieties comparatively. Research findings show that the RICAL algorithm may yield around 90% accuracy based on the unseen ground data. But using different vegetation indices would not significantly improve the final quality of the spectral discrimination and classification. Such practices may lead to the formulation of more effective management strategies

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),...

Deep Learning-Based Banknote Fitness Classification Using the Reflection Images by a Visible-Light One-Dimensional Line Image Sensor

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Tuyen Danh Pham

2018-02-01

Full Text Available In automatic paper currency sorting, fitness classification is a technique that assesses the quality of banknotes to determine whether a banknote is suitable for recirculation or should be replaced. Studies on using visible-light reflection images of banknotes for evaluating their usability have been reported. However, most of them were conducted under the assumption that the denomination and input direction of the banknote are predetermined. In other words, a pre-classification of the type of input banknote is required. To address this problem, we proposed a deep learning-based fitness-classification method that recognizes the fitness level of a banknote regardless of the denomination and input direction of the banknote to the system, using the reflection images of banknotes by visible-light one-dimensional line image sensor and a convolutional neural network (CNN. Experimental results on the banknote image databases of the Korean won (KRW and the Indian rupee (INR with three fitness levels, and the Unites States dollar (USD with two fitness levels, showed that our method gives better classification accuracy than other methods.

Deep Learning-Based Banknote Fitness Classification Using the Reflection Images by a Visible-Light One-Dimensional Line Image Sensor.

Science.gov (United States)

Pham, Tuyen Danh; Nguyen, Dat Tien; Kim, Wan; Park, Sung Ho; Park, Kang Ryoung

2018-02-06

In automatic paper currency sorting, fitness classification is a technique that assesses the quality of banknotes to determine whether a banknote is suitable for recirculation or should be replaced. Studies on using visible-light reflection images of banknotes for evaluating their usability have been reported. However, most of them were conducted under the assumption that the denomination and input direction of the banknote are predetermined. In other words, a pre-classification of the type of input banknote is required. To address this problem, we proposed a deep learning-based fitness-classification method that recognizes the fitness level of a banknote regardless of the denomination and input direction of the banknote to the system, using the reflection images of banknotes by visible-light one-dimensional line image sensor and a convolutional neural network (CNN). Experimental results on the banknote image databases of the Korean won (KRW) and the Indian rupee (INR) with three fitness levels, and the Unites States dollar (USD) with two fitness levels, showed that our method gives better classification accuracy than other methods.

7 CFR 27.73 - Supervision of transfers of cotton.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Supervision of transfers of cotton. 27.73 Section 27.73 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...

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.

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.

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.

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

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.

Towards automatic lithological classification from remote sensing data using support vector machines

Science.gov (United States)

Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

2010-05-01

Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14

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)

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.

Neural networks applied to the classification of remotely sensed data

NARCIS (Netherlands)

Mulder, Nanno; Spreeuwers, Lieuwe Jan

1991-01-01

A neural network with topology 2-8-8 is evaluated against the standard of supervised non-parametric maximum likelihood classification. The purpose of the evaluation is to compare the performance in terms of training speed and quality of classification. Classification is done on multispectral data

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

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.

Experiments on Supervised Learning Algorithms for Text Categorization

Science.gov (United States)

Namburu, Setu Madhavi; Tu, Haiying; Luo, Jianhui; Pattipati, Krishna R.

2005-01-01

Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major concern in many areas. While the general information may also include images and voice, we focus on the categorization of text data in this paper. We provide a brief overview of the information processing flow for text categorization, and discuss two supervised learning algorithms, viz., support vector machines (SVM) and partial least squares (PLS), which have been successfully applied in other domains, e.g., fault diagnosis [9]. While SVM has been well explored for binary classification and was reported as an efficient algorithm for text categorization, PLS has not yet been applied to text categorization. Our experiments are conducted on three data sets: Reuter's- 21578 dataset about corporate mergers and data acquisitions (ACQ), WebKB and the 20-Newsgroups. Results show that the performance of PLS is comparable to SVM in text categorization. A major drawback of SVM for multi-class categorization is that it requires a voting scheme based on the results of pair-wise classification. PLS does not have this drawback and could be a better candidate for multi-class text categorization.

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.

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.

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

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.

Conditional High-Order Boltzmann Machines for Supervised Relation Learning.

Science.gov (United States)

Huang, Yan; Wang, Wei; Wang, Liang; Tan, Tieniu

2017-09-01

Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.

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.

A new web-based system for unsupervised classification of satellite images from the Google Maps engine

Science.gov (United States)

Ferrán, Ángel; Bernabé, Sergio; García-Rodríguez, Pablo; Plaza, Antonio

2012-10-01

In this paper, we develop a new web-based system for unsupervised classification of satellite images available from the Google Maps engine. The system has been developed using the Google Maps API and incorporates functionalities such as unsupervised classification of image portions selected by the user (at the desired zoom level). For this purpose, we use a processing chain made up of the well-known ISODATA and k-means algorithms, followed by spatial post-processing based on majority voting. The system is currently hosted on a high performance server which performs the execution of classification algorithms and returns the obtained classification results in a very efficient way. The previous functionalities are necessary to use efficient techniques for the classification of images and the incorporation of content-based image retrieval (CBIR). Several experimental validation types of the classification results with the proposed system are performed by comparing the classification accuracy of the proposed chain by means of techniques available in the well-known Environment for Visualizing Images (ENVI) software package. The server has access to a cluster of commodity graphics processing units (GPUs), hence in future work we plan to perform the processing in parallel by taking advantage of the cluster.

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.)

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.)

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

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

Directory of Open Access Journals (Sweden)

Feng Qin

Full Text Available Common leaf spot (caused by Pseudopeziza medicaginis, rust (caused by Uromyces striatus, Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana and Cercospora leaf spot (caused by Cercospora medicaginis are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis. After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection, disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features was the optimal model. For this SVM model, the

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

Science.gov (United States)

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the

Discriminative semi-supervised feature selection via manifold regularization.

Science.gov (United States)

Xu, Zenglin; King, Irwin; Lyu, Michael Rung-Tsong; Jin, Rong

2010-07-01

Feature selection has attracted a huge amount of interest in both research and application communities of data mining. We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number of labeled samples are usually insufficient for identifying the relevant features, the critical problem arising from semi-supervised feature selection is how to take advantage of the information underneath the unlabeled data. To address this problem, we propose a novel discriminative semi-supervised feature selection method based on the idea of manifold regularization. The proposed approach selects features through maximizing the classification margin between different classes and simultaneously exploiting the geometry of the probability distribution that generates both labeled and unlabeled data. In comparison with previous semi-supervised feature selection algorithms, our proposed semi-supervised feature selection method is an embedded feature selection method and is able to find more discriminative features. We formulate the proposed feature selection method into a convex-concave optimization problem, where the saddle point corresponds to the optimal solution. To find the optimal solution, the level method, a fairly recent optimization method, is employed. We also present a theoretic proof of the convergence rate for the application of the level method to our problem. Empirical evaluation on several benchmark data sets demonstrates the effectiveness of the proposed semi-supervised feature selection method.

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.

Rule-based land cover classification from very high-resolution satellite image with multiresolution segmentation

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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.

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.

CLASSIFICATION AND RECOGNITION OF TOMB INFORMATION IN HYPERSPECTRAL IMAGE

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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.

SU-E-J-107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

International Nuclear Information System (INIS)

Bredfeldt, J; Liu, Y; Conklin, M; Keely, P; Eliceiri, K; Mackie, T

2014-01-01

Purpose: Our goal is to develop and apply a set of optical and computational tools to enable large-scale investigations of the interaction between collagen and tumor cells. Methods: We have built a novel imaging system for automating the capture of whole-slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi-automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). This group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups. Results: We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells. Conclusion: We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays

SU-E-J-107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

Energy Technology Data Exchange (ETDEWEB)

Bredfeldt, J; Liu, Y; Conklin, M; Keely, P; Eliceiri, K; Mackie, T [University of Wisconsin, Madison, WI (United States)

2014-06-01

Purpose: Our goal is to develop and apply a set of optical and computational tools to enable large-scale investigations of the interaction between collagen and tumor cells. Methods: We have built a novel imaging system for automating the capture of whole-slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi-automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). This group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups. Results: We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells. Conclusion: We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.

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

A semi-supervised approach using label propagation to support citation screening.

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Kontonatsios, Georgios; Brockmeier, Austin J; Przybyła, Piotr; McNaught, John; Mu, Tingting; Goulermas, John Y; Ananiadou, Sophia

2017-08-01

Citation screening, an integral process within systematic reviews that identifies citations relevant to the underlying research question, is a time-consuming and resource-intensive task. During the screening task, analysts manually assign a label to each citation, to designate whether a citation is eligible for inclusion in the review. Recently, several studies have explored the use of active learning in text classification to reduce the human workload involved in the screening task. However, existing approaches require a significant amount of manually labelled citations for the text classification to achieve a robust performance. In this paper, we propose a semi-supervised method that identifies relevant citations as early as possible in the screening process by exploiting the pairwise similarities between labelled and unlabelled citations to improve the classification performance without additional manual labelling effort. Our approach is based on the hypothesis that similar citations share the same label (e.g., if one citation should be included, then other similar citations should be included also). To calculate the similarity between labelled and unlabelled citations we investigate two different feature spaces, namely a bag-of-words and a spectral embedding based on the bag-of-words. The semi-supervised method propagates the classification codes of manually labelled citations to neighbouring unlabelled citations in the feature space. The automatically labelled citations are combined with the manually labelled citations to form an augmented training set. For evaluation purposes, we apply our method to reviews from clinical and public health. The results show that our semi-supervised method with label propagation achieves statistically significant improvements over two state-of-the-art active learning approaches across both clinical and public health reviews. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Mid-level image representations for real-time heart view plane classification of echocardiograms.

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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.

Optimizing area under the ROC curve using semi-supervised learning.

Science.gov (United States)

Wang, Shijun; Li, Diana; Petrick, Nicholas; Sahiner, Berkman; Linguraru, Marius George; Summers, Ronald M

2015-01-01

Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.

Fuzzy C-means classification for corrosion evolution of steel images

Science.gov (United States)

Trujillo, Maite; Sadki, Mustapha

2004-05-01

An unavoidable problem of metal structures is their exposure to rust degradation during their operational life. Thus, the surfaces need to be assessed in order to avoid potential catastrophes. There is considerable interest in the use of patch repair strategies which minimize the project costs. However, to operate such strategies with confidence in the long useful life of the repair, it is essential that the condition of the existing coatings and the steel substrate can be accurately quantified and classified. This paper describes the application of fuzzy set theory for steel surfaces classification according to the steel rust time. We propose a semi-automatic technique to obtain image clustering using the Fuzzy C-means (FCM) algorithm and we analyze two kinds of data to study the classification performance. Firstly, we investigate the use of raw images" pixels without any pre-processing methods and neighborhood pixels. Secondly, we apply Gaussian noise to the images with different standard deviation to study the FCM method tolerance to Gaussian noise. The noisy images simulate the possible perturbations of the images due to the weather or rust deposits in the steel surfaces during typical on-site acquisition procedures