Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy system.

Science.gov (United States)

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-10-23

A field imaging spectrometer system (FISS; 380-870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%-35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

Directory of Open Access Journals (Sweden)

Dhanoa Jaspreet Singh

2016-01-01

Full Text Available With the advent of technology and multimedia information, digital images are increasing very quickly. Various techniques are being developed to retrieve/search digital information or data contained in the image. Traditional Text Based Image Retrieval System is not plentiful. Since it is time consuming as it require manual image annotation. Also, the image annotation differs with different peoples. An alternate to this is Content Based Image Retrieval (CBIR system. It retrieves/search for image using its contents rather the text, keywords etc. A lot of exploration has been compassed in the range of Content Based Image Retrieval (CBIR with various feature extraction techniques. Shape is a significant image feature as it reflects the human perception. Moreover, Shape is quite simple to use by the user to define object in an image as compared to other features such as Color, texture etc. Over and above, if applied alone, no descriptor will give fruitful results. Further, by combining it with an improved classifier, one can use the positive features of both the descriptor and classifier. So, a tryout will be made to establish an algorithm for accurate feature (Shape extraction in Content Based Image Retrieval (CBIR. The main objectives of this project are: (a To propose an algorithm for shape feature extraction using CBIR, (b To evaluate the performance of proposed algorithm and (c To compare the proposed algorithm with state of art techniques.

Parallel content-based sub-image retrieval using hierarchical searching.

Science.gov (United States)

Yang, Lin; Qi, Xin; Xing, Fuyong; Kurc, Tahsin; Saltz, Joel; Foran, David J

2014-04-01

The capacity to systematically search through large image collections and ensembles and detect regions exhibiting similar morphological characteristics is central to pathology diagnosis. Unfortunately, the primary methods used to search digitized, whole-slide histopathology specimens are slow and prone to inter- and intra-observer variability. The central objective of this research was to design, develop, and evaluate a content-based image retrieval system to assist doctors for quick and reliable content-based comparative search of similar prostate image patches. Given a representative image patch (sub-image), the algorithm will return a ranked ensemble of image patches throughout the entire whole-slide histology section which exhibits the most similar morphologic characteristics. This is accomplished by first performing hierarchical searching based on a newly developed hierarchical annular histogram (HAH). The set of candidates is then further refined in the second stage of processing by computing a color histogram from eight equally divided segments within each square annular bin defined in the original HAH. A demand-driven master-worker parallelization approach is employed to speed up the searching procedure. Using this strategy, the query patch is broadcasted to all worker processes. Each worker process is dynamically assigned an image by the master process to search for and return a ranked list of similar patches in the image. The algorithm was tested using digitized hematoxylin and eosin (H&E) stained prostate cancer specimens. We have achieved an excellent image retrieval performance. The recall rate within the first 40 rank retrieved image patches is ∼90%. Both the testing data and source code can be downloaded from http://pleiad.umdnj.edu/CBII/Bioinformatics/.

A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

Science.gov (United States)

Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

2018-01-01

The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

Implementation and evaluation of a medical image management system with content-based retrieval support

International Nuclear Information System (INIS)

Carita, Edilson Carlos; Seraphim, Enzo; Honda, Marcelo Ossamu; Azevedo-Marques, Paulo Mazzoncini de

2008-01-01

Objective: the present paper describes the implementation and evaluation of a medical images management system with content-based retrieval support (PACS-CBIR) integrating modules focused on images acquisition, storage and distribution, and text retrieval by keyword and images retrieval by similarity. Materials and methods: internet-compatible technologies were utilized for the system implementation with free ware, and C ++ , PHP and Java languages on a Linux platform. There is a DICOM-compatible image management module and two query modules, one of them based on text and the other on similarity of image texture attributes. Results: results demonstrate an appropriate images management and storage, and that the images retrieval time, always < 15 sec, was found to be good by users. The evaluation of retrieval by similarity has demonstrated that the selected images extractor allowed the sorting of images according to anatomical areas. Conclusion: based on these results, one can conclude that the PACS-CBIR implementation is feasible. The system has demonstrated to be DICOM-compatible, and that it can be integrated with the local information system. The similar images retrieval functionality can be enhanced by the introduction of further descriptors. (author)

A review of content-based image retrieval systems in medical applications-clinical benefits and future directions.

Science.gov (United States)

Müller, Henning; Michoux, Nicolas; Bandon, David; Geissbuhler, Antoine

2004-02-01

Content-based visual information retrieval (CBVIR) or content-based image retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. The availability of large and steadily growing amounts of visual and multimedia data, and the development of the Internet underline the need to create thematic access methods that offer more than simple text-based queries or requests based on matching exact database fields. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of differing sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered. In the medical field, images, and especially digital images, are produced in ever-increasing quantities and used for diagnostics and therapy. The Radiology Department of the University Hospital of Geneva alone produced more than 12,000 images a day in 2002. The cardiology is currently the second largest producer of digital images, especially with videos of cardiac catheterization ( approximately 1800 exams per year containing almost 2000 images each). The total amount of cardiologic image data produced in the Geneva University Hospital was around 1 TB in 2002. Endoscopic videos can equally produce enormous amounts of data. With digital imaging and communications in medicine (DICOM), a standard for image communication has been set and patient information can be stored with the actual image(s), although still a few problems prevail with respect to the standardization. In several articles, content-based access to medical images for supporting clinical decision-making has been proposed that would ease the management of clinical data and scenarios for the integration of

Ontology of Gaps in Content-Based Image Retrieval

OpenAIRE

Deserno, Thomas M.; Antani, Sameer; Long, Rodney

2008-01-01

Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however, has not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed (without supporting analysis) to the ina...

Physical retrieval of precipitation water contents from Special Sensor Microwave/Imager (SSM/I) data. Part 2: Retrieval method and applications (report version)

Science.gov (United States)

Olson, William S.

1990-01-01

A physical retrieval method for estimating precipitating water distributions and other geophysical parameters based upon measurements from the DMSP-F8 SSM/I is developed. Three unique features of the retrieval method are (1) sensor antenna patterns are explicitly included to accommodate varying channel resolution; (2) precipitation-brightness temperature relationships are quantified using the cloud ensemble/radiative parameterization; and (3) spatial constraints are imposed for certain background parameters, such as humidity, which vary more slowly in the horizontal than the cloud and precipitation water contents. The general framework of the method will facilitate the incorporation of measurements from the SSMJT, SSM/T-2 and geostationary infrared measurements, as well as information from conventional sources (e.g., radiosondes) or numerical forecast model fields.

Automating the construction of scene classifiers for content-based video retrieval

NARCIS (Netherlands)

Khan, L.; Israël, Menno; Petrushin, V.A.; van den Broek, Egon; van der Putten, Peter

2004-01-01

This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a

Ontology of gaps in content-based image retrieval.

Science.gov (United States)

Deserno, Thomas M; Antani, Sameer; Long, Rodney

2009-04-01

Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however, has not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed (without supporting analysis) to the inability of these applications in overcoming the "semantic gap." The semantic gap divides the high-level scene understanding and interpretation available with human cognitive capabilities from the low-level pixel analysis of computers, based on mathematical processing and artificial intelligence methods. In this paper, we suggest a more systematic and comprehensive view of the concept of "gaps" in medical CBIR research. In particular, we define an ontology of 14 gaps that addresses the image content and features, as well as system performance and usability. In addition to these gaps, we identify seven system characteristics that impact CBIR applicability and performance. The framework we have created can be used a posteriori to compare medical CBIR systems and approaches for specific biomedical image domains and goals and a priori during the design phase of a medical CBIR application, as the systematic analysis of gaps provides detailed insight in system comparison and helps to direct future research.

Extending a DBMS to Support Content-Based Video Retrieval : A Formula 1 Case Study

NARCIS (Netherlands)

Petkovic, M.; Jonker, Willem; Mihajlovic, V.

Content-based retrieval has been identified as one of the most challenging problems, requiring a multidisciplinary research among computer vision, information retrieval, artificial intelligence, database, and other fields. In this paper, we address the specific aspect of inferring semantics

Three-dimensional spatiotemporal features for fast content-based retrieval of focal liver lesions.

Science.gov (United States)

Roy, Sharmili; Chi, Yanling; Liu, Jimin; Venkatesh, Sudhakar K; Brown, Michael S

2014-11-01

Content-based image retrieval systems for 3-D medical datasets still largely rely on 2-D image-based features extracted from a few representative slices of the image stack. Most 2 -D features that are currently used in the literature not only model a 3-D tumor incompletely but are also highly expensive in terms of computation time, especially for high-resolution datasets. Radiologist-specified semantic labels are sometimes used along with image-based 2-D features to improve the retrieval performance. Since radiological labels show large interuser variability, are often unstructured, and require user interaction, their use as lesion characterizing features is highly subjective, tedious, and slow. In this paper, we propose a 3-D image-based spatiotemporal feature extraction framework for fast content-based retrieval of focal liver lesions. All the features are computer generated and are extracted from four-phase abdominal CT images. Retrieval performance and query processing times for the proposed framework is evaluated on a database of 44 hepatic lesions comprising of five pathological types. Bull's eye percentage score above 85% is achieved for three out of the five lesion pathologies and for 98% of query lesions, at least one same type of lesion is ranked among the top two retrieved results. Experiments show that the proposed system's query processing is more than 20 times faster than other already published systems that use 2-D features. With fast computation time and high retrieval accuracy, the proposed system has the potential to be used as an assistant to radiologists for routine hepatic tumor diagnosis.

Content-aware network storage system supporting metadata retrieval

Science.gov (United States)

Liu, Ke; Qin, Leihua; Zhou, Jingli; Nie, Xuejun

2008-12-01

Nowadays, content-based network storage has become the hot research spot of academy and corporation[1]. In order to solve the problem of hit rate decline causing by migration and achieve the content-based query, we exploit a new content-aware storage system which supports metadata retrieval to improve the query performance. Firstly, we extend the SCSI command descriptor block to enable system understand those self-defined query requests. Secondly, the extracted metadata is encoded by extensible markup language to improve the universality. Thirdly, according to the demand of information lifecycle management (ILM), we store those data in different storage level and use corresponding query strategy to retrieval them. Fourthly, as the file content identifier plays an important role in locating data and calculating block correlation, we use it to fetch files and sort query results through friendly user interface. Finally, the experiments indicate that the retrieval strategy and sort algorithm have enhanced the retrieval efficiency and precision.

Indexing, learning and content-based retrieval for special purpose image databases

NARCIS (Netherlands)

M.J. Huiskes (Mark); E.J. Pauwels (Eric)

2005-01-01

textabstractThis chapter deals with content-based image retrieval in special purpose image databases. As image data is amassed ever more effortlessly, building efficient systems for searching and browsing of image databases becomes increasingly urgent. We provide an overview of the current

The utilization of human color categorization for content-based image retrieval

NARCIS (Netherlands)

van den Broek, Egon; Rogowitz, Bernice E.; Kisters, Peter M.F.; Pappas, Thrasyvoulos N.; Vuurpijl, Louis G.

2004-01-01

We present the concept of intelligent Content-Based Image Retrieval (iCBIR), which incorporates knowledge concerning human cognition in system development. The present research focuses on the utilization of color categories (or focal colors) for CBIR purposes, in particularly considered to be useful

Content-based retrieval in videos from laparoscopic surgery

Science.gov (United States)

Schoeffmann, Klaus; Beecks, Christian; Lux, Mathias; Uysal, Merih Seran; Seidl, Thomas

2016-03-01

In the field of medical endoscopy more and more surgeons are changing over to record and store videos of their endoscopic procedures for long-term archival. These endoscopic videos are a good source of information for explanations to patients and follow-up operations. As the endoscope is the "eye of the surgeon", the video shows the same information the surgeon has seen during the operation, and can describe the situation inside the patient much more precisely than an operation report would do. Recorded endoscopic videos can also be used for training young surgeons and in some countries the long-term archival of video recordings from endoscopic procedures is even enforced by law. A major challenge, however, is to efficiently access these very large video archives for later purposes. One problem, for example, is to locate specific images in the videos that show important situations, which are additionally captured as static images during the procedure. This work addresses this problem and focuses on contentbased video retrieval in data from laparoscopic surgery. We propose to use feature signatures, which can appropriately and concisely describe the content of laparoscopic images, and show that by using this content descriptor with an appropriate metric, we are able to efficiently perform content-based retrieval in laparoscopic videos. In a dataset with 600 captured static images from 33 hours recordings, we are able to find the correct video segment for more than 88% of these images.

Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

Science.gov (United States)

Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Gao, Yang; Chen, Yang; Feng, Qianjin; Chen, Wufan; Lu, Zhentai

2014-01-01

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.

Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

Directory of Open Access Journals (Sweden)

Meiyan Huang

Full Text Available This study aims to develop content-based image retrieval (CBIR system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor. Using the BoVW model with partition learning, the mean average precision (mAP of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.

Content-Based Image Retrieval Benchmarking: Utilizing color categories and color distributions

NARCIS (Netherlands)

van den Broek, Egon; Kisters, Peter M.F.; Vuurpijl, Louis G.

From a human centered perspective three ingredients for Content-Based Image Retrieval (CBIR) were developed. First, with their existence confirmed by experimental data, 11 color categories were utilized for CBIR and used as input for a new color space segmentation technique. The complete HSI color

Optimizing top precision performance measure of content-based image retrieval by learning similarity function

KAUST Repository

Liang, Ru-Ze

2017-04-24

In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

Optimizing top precision performance measure of content-based image retrieval by learning similarity function

KAUST Repository

Liang, Ru-Ze; Shi, Lihui; Wang, Haoxiang; Meng, Jiandong; Wang, Jim Jing-Yan; Sun, Qingquan; Gu, Yi

2017-01-01

In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

Relevance Feedback in Content Based Image Retrieval: A Review

Directory of Open Access Journals (Sweden)

Manesh B. Kokare

2011-01-01

Full Text Available This paper provides an overview of the technical achievements in the research area of relevance feedback (RF in content-based image retrieval (CBIR. Relevance feedback is a powerful technique in CBIR systems, in order to improve the performance of CBIR effectively. It is an open research area to the researcher to reduce the semantic gap between low-level features and high level concepts. The paper covers the current state of art of the research in relevance feedback in CBIR, various relevance feedback techniques and issues in relevance feedback are discussed in detail.

Wavelet optimization for content-based image retrieval in medical databases.

Science.gov (United States)

Quellec, G; Lamard, M; Cazuguel, G; Cochener, B; Roux, C

2010-04-01

We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system. Copyright 2009 Elsevier B.V. All rights reserved.

[A retrieval method of drug molecules based on graph collapsing].

Science.gov (United States)

Qu, J W; Lv, X Q; Liu, Z M; Liao, Y; Sun, P H; Wang, B; Tang, Z

2018-04-18

To establish a compact and efficient hypergraph representation and a graph-similarity-based retrieval method of molecules to achieve effective and efficient medicine information retrieval. Chemical structural formula (CSF) was a primary search target as a unique and precise identifier for each compound at the molecular level in the research field of medicine information retrieval. To retrieve medicine information effectively and efficiently, a complete workflow of the graph-based CSF retrieval system was introduced. This system accepted the photos taken from smartphones and the sketches drawn on tablet personal computers as CSF inputs, and formalized the CSFs with the corresponding graphs. Then this paper proposed a compact and efficient hypergraph representation for molecules on the basis of analyzing factors that directly affected the efficiency of graph matching. According to the characteristics of CSFs, a hierarchical collapsing method combining graph isomorphism and frequent subgraph mining was adopted. There was yet a fundamental challenge, subgraph overlapping during the collapsing procedure, which hindered the method from establishing the correct compact hypergraph of an original CSF graph. Therefore, a graph-isomorphism-based algorithm was proposed to select dominant acyclic subgraphs on the basis of overlapping analysis. Finally, the spatial similarity among graphical CSFs was evaluated by multi-dimensional measures of similarity. To evaluate the performance of the proposed method, the proposed system was firstly compared with Wikipedia Chemical Structure Explorer (WCSE), the state-of-the-art system that allowed CSF similarity searching within Wikipedia molecules dataset, on retrieval accuracy. The system achieved higher values on mean average precision, discounted cumulative gain, rank-biased precision, and expected reciprocal rank than WCSE from the top-2 to the top-10 retrieved results. Specifically, the system achieved 10%, 1.41, 6.42%, and 1

ImageGrouper: a group-oriented user interface for content-based image retrieval and digital image arrangement

NARCIS (Netherlands)

Nakazato, Munehiro; Manola, L.; Huang, Thomas S.

In content-based image retrieval (CBIR), experimental (trial-and-error) query with relevance feedback is essential for successful retrieval. Unfortunately, the traditional user interfaces are not suitable for trying different combinations of query examples. This is because first, these systems

Multiscale Distance Coherence Vector Algorithm for Content-Based Image Retrieval

Science.gov (United States)

Jiexian, Zeng; Xiupeng, Liu

2014-01-01

Multiscale distance coherence vector algorithm for content-based image retrieval (CBIR) is proposed due to the same descriptor with different shapes and the shortcomings of antinoise performance of the distance coherence vector algorithm. By this algorithm, the image contour curve is evolved by Gaussian function first, and then the distance coherence vector is, respectively, extracted from the contour of the original image and evolved images. Multiscale distance coherence vector was obtained by reasonable weight distribution of the distance coherence vectors of evolved images contour. This algorithm not only is invariable to translation, rotation, and scaling transformation but also has good performance of antinoise. The experiment results show us that the algorithm has a higher recall rate and precision rate for the retrieval of images polluted by noise. PMID:24883416

An efficient and robutst method for shape-based image retrieval

International Nuclear Information System (INIS)

Salih, N.D.; Besar, R.; Abas, F.S.

2007-01-01

Shapes can be thought as being the words oft he visual language. Shape boundaries need to be simplified and estimated in a wide variety of image analysis applications. Representation and description of Shapes is one of the major problems in content-based image retrieval (CBIR). This paper present an a novel method for shape representation and description named block-based shape representation (BSR), which is capable of extracting reliable information of the object outline in a concise manner. Our technique is translation, scale, and rotation invariant. It works well on different types of shapes and fast enough for use in real-time. This technique has been implemented and evaluated in order to analyze its accuracy and Efficiency. Based on the experimental results, we urge that the proposed BSR is a compact and reliable shape representation method. (author)

Improved image retrieval based on fuzzy colour feature vector

Science.gov (United States)

Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

2013-03-01

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

Semantic-based surveillance video retrieval.

Science.gov (United States)

Hu, Weiming; Xie, Dan; Fu, Zhouyu; Zeng, Wenrong; Maybank, Steve

2007-04-01

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene.

Studies and Application of Remote Sensing Retrieval Method of Soil Moisture Content in Land Parcel Units in Irrigation Area

Science.gov (United States)

Zhu, H.; Zhao, H. L.; Jiang, Y. Z.; Zang, W. B.

2018-05-01

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data

Dual-force ISOMAP: a new relevance feedback method for medical image retrieval.

Science.gov (United States)

Shen, Hualei; Tao, Dacheng; Ma, Dianfu

2013-01-01

With great potential for assisting radiological image interpretation and decision making, content-based image retrieval in the medical domain has become a hot topic in recent years. Many methods to enhance the performance of content-based medical image retrieval have been proposed, among which the relevance feedback (RF) scheme is one of the most promising. Given user feedback information, RF algorithms interactively learn a user's preferences to bridge the "semantic gap" between low-level computerized visual features and high-level human semantic perception and thus improve retrieval performance. However, most existing RF algorithms perform in the original high-dimensional feature space and ignore the manifold structure of the low-level visual features of images. In this paper, we propose a new method, termed dual-force ISOMAP (DFISOMAP), for content-based medical image retrieval. Under the assumption that medical images lie on a low-dimensional manifold embedded in a high-dimensional ambient space, DFISOMAP operates in the following three stages. First, the geometric structure of positive examples in the learned low-dimensional embedding is preserved according to the isometric feature mapping (ISOMAP) criterion. To precisely model the geometric structure, a reconstruction error constraint is also added. Second, the average distance between positive and negative examples is maximized to separate them; this margin maximization acts as a force that pushes negative examples far away from positive examples. Finally, the similarity propagation technique is utilized to provide negative examples with another force that will pull them back into the negative sample set. We evaluate the proposed method on a subset of the IRMA medical image dataset with a RF-based medical image retrieval framework. Experimental results show that DFISOMAP outperforms popular approaches for content-based medical image retrieval in terms of accuracy and stability.

Surfing for suicide methods and help: content analysis of websites retrieved with search engines in Austria and the United States.

Science.gov (United States)

Till, Benedikt; Niederkrotenthaler, Thomas

2014-08-01

The Internet provides a variety of resources for individuals searching for suicide-related information. Structured content-analytic approaches to assess intercultural differences in web contents retrieved with method-related and help-related searches are scarce. We used the 2 most popular search engines (Google and Yahoo/Bing) to retrieve US-American and Austrian search results for the term suicide, method-related search terms (e.g., suicide methods, how to kill yourself, painless suicide, how to hang yourself), and help-related terms (e.g., suicidal thoughts, suicide help) on February 11, 2013. In total, 396 websites retrieved with US search engines and 335 websites from Austrian searches were analyzed with content analysis on the basis of current media guidelines for suicide reporting. We assessed the quality of websites and compared findings across search terms and between the United States and Austria. In both countries, protective outweighed harmful website characteristics by approximately 2:1. Websites retrieved with method-related search terms (e.g., how to hang yourself) contained more harmful (United States: P search engines generally had more protective characteristics (P search engines. Resources with harmful characteristics were better ranked than those with protective characteristics (United States: P < .01, Austria: P < .05). The quality of suicide-related websites obtained depends on the search terms used. Preventive efforts to improve the ranking of preventive web content, particularly regarding method-related search terms, seem necessary. © Copyright 2014 Physicians Postgraduate Press, Inc.

A passage retrieval method based on probabilistic information retrieval model and UMLS concepts in biomedical question answering.

Science.gov (United States)

Sarrouti, Mourad; Ouatik El Alaoui, Said

2017-04-01

Passage retrieval, the identification of top-ranked passages that may contain the answer for a given biomedical question, is a crucial component for any biomedical question answering (QA) system. Passage retrieval in open-domain QA is a longstanding challenge widely studied over the last decades. However, it still requires further efforts in biomedical QA. In this paper, we present a new biomedical passage retrieval method based on Stanford CoreNLP sentence/passage length, probabilistic information retrieval (IR) model and UMLS concepts. In the proposed method, we first use our document retrieval system based on PubMed search engine and UMLS similarity to retrieve relevant documents to a given biomedical question. We then take the abstracts from the retrieved documents and use Stanford CoreNLP for sentence splitter to make a set of sentences, i.e., candidate passages. Using stemmed words and UMLS concepts as features for the BM25 model, we finally compute the similarity scores between the biomedical question and each of the candidate passages and keep the N top-ranked ones. Experimental evaluations performed on large standard datasets, provided by the BioASQ challenge, show that the proposed method achieves good performances compared with the current state-of-the-art methods. The proposed method significantly outperforms the current state-of-the-art methods by an average of 6.84% in terms of mean average precision (MAP). We have proposed an efficient passage retrieval method which can be used to retrieve relevant passages in biomedical QA systems with high mean average precision. Copyright © 2017 Elsevier Inc. All rights reserved.

Content-based retrieval of brain tumor in contrast-enhanced MRI images using tumor margin information and learned distance metric.

Science.gov (United States)

Yang, Wei; Feng, Qianjin; Yu, Mei; Lu, Zhentai; Gao, Yang; Xu, Yikai; Chen, Wufan

2012-11-01

A content-based image retrieval (CBIR) method for T1-weighted contrast-enhanced MRI (CE-MRI) images of brain tumors is presented for diagnosis aid. The method is thoroughly evaluated on a large image dataset. Using the tumor region as a query, the authors' CBIR system attempts to retrieve tumors of the same pathological category. Aside from commonly used features such as intensity, texture, and shape features, the authors use a margin information descriptor (MID), which is capable of describing the characteristics of tissue surrounding a tumor, for representing image contents. In addition, the authors designed a distance metric learning algorithm called Maximum mean average Precision Projection (MPP) to maximize the smooth approximated mean average precision (mAP) to optimize retrieval performance. The effectiveness of MID and MPP algorithms was evaluated using a brain CE-MRI dataset consisting of 3108 2D scans acquired from 235 patients with three categories of brain tumors (meningioma, glioma, and pituitary tumor). By combining MID and other features, the mAP of retrieval increased by more than 6% with the learned distance metrics. The distance metric learned by MPP significantly outperformed the other two existing distance metric learning methods in terms of mAP. The CBIR system using the proposed strategies achieved a mAP of 87.3% and a precision of 89.3% when top 10 images were returned by the system. Compared with scale-invariant feature transform, the MID, which uses the intensity profile as descriptor, achieves better retrieval performance. Incorporating tumor margin information represented by MID with the distance metric learned by the MPP algorithm can substantially improve the retrieval performance for brain tumors in CE-MRI.

Automatic content linking: Speech-based just-in-time retrieval for multimedia archives

NARCIS (Netherlands)

Popescu-Belis, A.; Kilgour, J.; Poller, P.; Nanchen, A.; Boertjes, E.; Wit, J. de

2010-01-01

The Automatic Content Linking Device monitors a conversation and uses automatically recognized words to retrieve documents that are of potential use to the participants. The document set includes project related reports or emails, transcribed snippets of past meetings, and websites. Retrieval

View-based 3-D object retrieval

CERN Document Server

Gao, Yue

2014-01-01

Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging res

A new Dobson Umkehr ozone profile retrieval method optimising information content and resolution

Science.gov (United States)

Stone, K.; Tully, M. B.; Rhodes, S. K.; Schofield, R.

2015-03-01

The standard Dobson Umkehr methodology to retrieve coarse-resolution ozone profiles used by the National Oceanographic and Atmospheric Administration uses designated solar zenith angles (SZAs). However, some information may be lost if measurements lie outside the designated SZA range (between 60° and 90°), or do not conform to the fitting technique. Also, while Umkehr measurements can be taken using multiple wavelength pairs (A, C and D), past retrieval methods have focused on a single pair (C). Here we present an Umkehr inversion method that uses measurements at all SZAs (termed operational) and all wavelength pairs. (Although, we caution direct comparison to other algorithms.) Information content for a Melbourne, Australia (38° S, 145° E) Umkehr measurement case study from 28 January 1994, with SZA range similar to that designated in previous algorithms is shown. When comparing the typical single wavelength pair with designated SZAs to the operational measurements, the total degrees of freedom (independent pieces of information) increases from 3.1 to 3.4, with the majority of the information gain originating from Umkehr layers 2 + 3 and 4 (10-20 km and 25-30 km respectively). In addition to this, using all available wavelength pairs increases the total degrees of freedom to 5.2, with the most significant increases in Umkehr layers 2 + 3 to 7 and 9+ (10-40 and 45-80 km). Investigating a case from 13 April 1970 where the measurements extend beyond the 90° SZA range gives further information gain, with total degrees of freedom extending to 6.5. Similar increases are seen in the information content. Comparing the retrieved Melbourne Umkehr time series with ozonesondes shows excellent agreement in layers 2 + 3 and 4 (10-20 and 25-30 km) for both C and A + C + D-pairs. Retrievals in layers 5 and 6 (25-30 and 30-35 km) consistently show lower ozone partial column compared to ozonesondes. This is likely due to stray light effects that are not accounted for in the

Content-based intermedia synchronization

Science.gov (United States)

Oh, Dong-Young; Sampath-Kumar, Srihari; Rangan, P. Venkat

1995-03-01

Inter-media synchronization methods developed until now have been based on syntactic timestamping of video frames and audio samples. These methods are not fully appropriate for the synchronization of multimedia objects which may have to be accessed individually by their contents, e.g. content-base data retrieval. We propose a content-based multimedia synchronization scheme in which a media stream is viewed as hierarchial composition of smaller objects which are logically structured based on the contents, and the synchronization is achieved by deriving temporal relations among logical units of media object. content-based synchronization offers several advantages such as, elimination of the need for time stamping, freedom from limitations of jitter, synchronization of independently captured media objects in video editing, and compensation for inherent asynchronies in capture times of video and audio.

Content-based image retrieval with ontological ranking

Science.gov (United States)

Tsai, Shen-Fu; Tsai, Min-Hsuan; Huang, Thomas S.

2010-02-01

Images are a much more powerful medium of expression than text, as the adage says: "One picture is worth a thousand words." It is because compared with text consisting of an array of words, an image has more degrees of freedom and therefore a more complicated structure. However, the less limited structure of images presents researchers in the computer vision community a tough task of teaching machines to understand and organize images, especially when a limit number of learning examples and background knowledge are given. The advance of internet and web technology in the past decade has changed the way human gain knowledge. People, hence, can exchange knowledge with others by discussing and contributing information on the web. As a result, the web pages in the internet have become a living and growing source of information. One is therefore tempted to wonder whether machines can learn from the web knowledge base as well. Indeed, it is possible to make computer learn from the internet and provide human with more meaningful knowledge. In this work, we explore this novel possibility on image understanding applied to semantic image search. We exploit web resources to obtain links from images to keywords and a semantic ontology constituting human's general knowledge. The former maps visual content to related text in contrast to the traditional way of associating images with surrounding text; the latter provides relations between concepts for machines to understand to what extent and in what sense an image is close to the image search query. With the aid of these two tools, the resulting image search system is thus content-based and moreover, organized. The returned images are ranked and organized such that semantically similar images are grouped together and given a rank based on the semantic closeness to the input query. The novelty of the system is twofold: first, images are retrieved not only based on text cues but their actual contents as well; second, the grouping

Design and development of a content-based medical image retrieval system for spine vertebrae irregularity.

Science.gov (United States)

Mustapha, Aouache; Hussain, Aini; Samad, Salina Abdul; Zulkifley, Mohd Asyraf; Diyana Wan Zaki, Wan Mimi; Hamid, Hamzaini Abdul

2015-01-16

Content-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities. In this paper, a more robust CBMIR system that deals with both cervical and lumbar vertebrae irregularity is afforded. It comprises three main phases, namely modelling, indexing and retrieval of the vertebrae image. The main tasks in the modelling phase are to improve and enhance the visibility of the x-ray image for better segmentation results using active shape model (ASM). The segmented vertebral fractures are then characterized in the indexing phase using region-based fracture characterization (RB-FC) and contour-based fracture characterization (CB-FC). Upon a query, the characterized features are compared to the query image. Effectiveness of the retrieval phase is determined by its retrieval, thus, we propose an integration of the predictor model based cross validation neural network (PMCVNN) and similarity matching (SM) in this stage. The PMCVNN task is to identify the correct vertebral irregularity class through classification allowing the SM process to be more efficient. Retrieval performance between the proposed and the standard retrieval architectures are then compared using retrieval precision (Pr@M) and average group score (AGS) measures. Experimental results show that the new integrated retrieval architecture performs better than those of the standard CBMIR architecture with retrieval results of cervical (AGS > 87%) and lumbar (AGS > 82%) datasets. The proposed CBMIR architecture shows encouraging results with high Pr@M accuracy. As a result, images from the same visualization class are returned for further used by the medical personnel.

A STUDY ON RANKING METHOD IN RETRIEVING WEB PAGES BASED ON CONTENT AND LINK ANALYSIS: COMBINATION OF FOURIER DOMAIN SCORING AND PAGERANK SCORING

Directory of Open Access Journals (Sweden)

Diana Purwitasari

2008-01-01

Full Text Available Ranking module is an important component of search process which sorts through relevant pages. Since collection of Web pages has additional information inherent in the hyperlink structure of the Web, it can be represented as link score and then combined with the usual information retrieval techniques of content score. In this paper we report our studies about ranking score of Web pages combined from link analysis, PageRank Scoring, and content analysis, Fourier Domain Scoring. Our experiments use collection of Web pages relate to Statistic subject from Wikipedia with objectives to check correctness and performance evaluation of combination ranking method. Evaluation of PageRank Scoring show that the highest score does not always relate to Statistic. Since the links within Wikipedia articles exists so that users are always one click away from more information on any point that has a link attached, it it possible that unrelated topics to Statistic are most likely frequently mentioned in the collection. While the combination method show link score which is given proportional weight to content score of Web pages does effect the retrieval results.

An efficient similarity measure for content based image retrieval using memetic algorithm

Directory of Open Access Journals (Sweden)

Mutasem K. Alsmadi

2017-06-01

Full Text Available Content based image retrieval (CBIR systems work by retrieving images which are related to the query image (QI from huge databases. The available CBIR systems extract limited feature sets which confine the retrieval efficacy. In this work, extensive robust and important features were extracted from the images database and then stored in the feature repository. This feature set is composed of color signature with the shape and color texture features. Where, features are extracted from the given QI in the similar fashion. Consequently, a novel similarity evaluation using a meta-heuristic algorithm called a memetic algorithm (genetic algorithm with great deluge is achieved between the features of the QI and the features of the database images. Our proposed CBIR system is assessed by inquiring number of images (from the test dataset and the efficiency of the system is evaluated by calculating precision-recall value for the results. The results were superior to other state-of-the-art CBIR systems in regard to precision.

RETRIEVAL OF AEROSOL MICROPHYSICAL PROPERTIES BASED ON THE OPTIMAL ESTIMATION METHOD: INFORMATION CONTENT ANALYSIS FOR SATELLITE POLARIMETRIC REMOTE SENSING MEASUREMENTS

Directory of Open Access Journals (Sweden)

W. Z. Hou

2018-04-01

Full Text Available This paper evaluates the information content for the retrieval of key aerosol microphysical and surface properties for multispectral single-viewing satellite polarimetric measurements cantered at 410, 443, 555, 670, 865, 1610 and 2250 nm over bright land. To conduct the information content analysis, the synthetic data are simulated by the Unified Linearized Vector Radiative Transfer Model (UNLVTM with the intensity and polarization together over bare soil surface for various scenarios. Following the optimal estimation theory, a principal component analysis method is employed to reconstruct the multispectral surface reflectance from 410 nm to 2250 nm, and then integrated with a linear one-parametric BPDF model to represent the contribution of polarized surface reflectance, thus further to decouple the surface-atmosphere contribution from the TOA measurements. Focusing on two different aerosol models with the aerosol optical depth equal to 0.8 at 550 nm, the total DFS and DFS component of each retrieval aerosol and surface parameter are analysed. The DFS results show that the key aerosol microphysical properties, such as the fine- and coarse-mode columnar volume concentration, the effective radius and the real part of complex refractive index at 550 nm, could be well retrieved with the surface parameters simultaneously over bare soil surface type. The findings of this study can provide the guidance to the inversion algorithm development over bright surface land by taking full use of the single-viewing satellite polarimetric measurements.

Retrieval of Aerosol Microphysical Properties Based on the Optimal Estimation Method: Information Content Analysis for Satellite Polarimetric Remote Sensing Measurements

Science.gov (United States)

Hou, W. Z.; Li, Z. Q.; Zheng, F. X.; Qie, L. L.

2018-04-01

This paper evaluates the information content for the retrieval of key aerosol microphysical and surface properties for multispectral single-viewing satellite polarimetric measurements cantered at 410, 443, 555, 670, 865, 1610 and 2250 nm over bright land. To conduct the information content analysis, the synthetic data are simulated by the Unified Linearized Vector Radiative Transfer Model (UNLVTM) with the intensity and polarization together over bare soil surface for various scenarios. Following the optimal estimation theory, a principal component analysis method is employed to reconstruct the multispectral surface reflectance from 410 nm to 2250 nm, and then integrated with a linear one-parametric BPDF model to represent the contribution of polarized surface reflectance, thus further to decouple the surface-atmosphere contribution from the TOA measurements. Focusing on two different aerosol models with the aerosol optical depth equal to 0.8 at 550 nm, the total DFS and DFS component of each retrieval aerosol and surface parameter are analysed. The DFS results show that the key aerosol microphysical properties, such as the fine- and coarse-mode columnar volume concentration, the effective radius and the real part of complex refractive index at 550 nm, could be well retrieved with the surface parameters simultaneously over bare soil surface type. The findings of this study can provide the guidance to the inversion algorithm development over bright surface land by taking full use of the single-viewing satellite polarimetric measurements.

Active learning methods for interactive image retrieval.

Science.gov (United States)

Gosselin, Philippe Henri; Cord, Matthieu

2008-07-01

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

Medical Content Searching, Retrieving, and Sharing Over the Internet: Lessons Learned From the mEducator Through a Scenario-Based Evaluation

Science.gov (United States)

Spachos, Dimitris; Mylläri, Jarkko; Giordano, Daniela; Dafli, Eleni; Mitsopoulou, Evangelia; Schizas, Christos N; Pattichis, Constantinos; Nikolaidou, Maria

2015-01-01

Background The mEducator Best Practice Network (BPN) implemented and extended standards and reference models in e-learning to develop innovative frameworks as well as solutions that enable specialized state-of-the-art medical educational content to be discovered, retrieved, shared, and re-purposed across European Institutions, targeting medical students, doctors, educators and health care professionals. Scenario-based evaluation for usability testing, complemented with data from online questionnaires and field notes of users’ performance, was designed and utilized for the evaluation of these solutions. Objective The objective of this work is twofold: (1) to describe one instantiation of the mEducator BPN solutions (mEducator3.0 - “MEdical Education LINnked Arena” MELINA+) with a focus on the metadata schema used, as well as on other aspects of the system that pertain to usability and acceptance, and (2) to present evaluation results on the suitability of the proposed metadata schema for searching, retrieving, and sharing of medical content and with respect to the overall usability and acceptance of the system from the target users. Methods A comprehensive evaluation methodology framework was developed and applied to four case studies, which were conducted in four different countries (ie, Greece, Cyprus, Bulgaria and Romania), with a total of 126 participants. In these case studies, scenarios referring to creating, sharing, and retrieving medical educational content using mEducator3.0 were used. The data were collected through two online questionnaires, consisting of 36 closed-ended questions and two open-ended questions that referred to mEducator 3.0 and through the use of field notes during scenario-based evaluations. Results The main findings of the study showed that even though the informational needs of the mEducator target groups were addressed to a satisfactory extent and the metadata schema supported content creation, sharing, and retrieval from an end

IMAGE DESCRIPTIONS FOR SKETCH BASED IMAGE RETRIEVAL

OpenAIRE

SAAVEDRA RONDO, JOSE MANUEL; SAAVEDRA RONDO, JOSE MANUEL

2008-01-01

Due to the massive use of Internet together with the proliferation of media devices, content based image retrieval has become an active discipline in computer science. A common content based image retrieval approach requires that the user gives a regular image (e.g, a photo) as a query. However, having a regular image as query may be a serious problem. Indeed, people commonly use an image retrieval system because they do not count on the desired image. An easy alternative way t...

Multimedia content analysis, management and retrieval: trends and challenges

Science.gov (United States)

Hanjalic, Alan; Sebe, Nicu; Chang, Edward

2006-01-01

Recent advances in computing, communications and storage technology have made multimedia data become prevalent. Multimedia has gained enormous potential in improving the processes in a wide range of fields, such as advertising and marketing, education and training, entertainment, medicine, surveillance, wearable computing, biometrics, and remote sensing. Rich content of multimedia data, built through the synergies of the information contained in different modalities, calls for new and innovative methods for modeling, processing, mining, organizing, and indexing of this data for effective and efficient searching, retrieval, delivery, management and sharing of multimedia content, as required by the applications in the abovementioned fields. The objective of this paper is to present our views on the trends that should be followed when developing such methods, to elaborate on the related research challenges, and to introduce the new conference, Multimedia Content Analysis, Management and Retrieval, as a premium venue for presenting and discussing these methods with the scientific community. Starting from 2006, the conference will be held annually as a part of the IS&T/SPIE Electronic Imaging event.

INTEGRATION OF SPATIAL INFORMATION WITH COLOR FOR CONTENT RETRIEVAL OF REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

Bikesh Kumar Singh

2010-08-01

Full Text Available There is rapid increase in image databases of remote sensing images due to image satellites with high resolution, commercial applications of remote sensing & high available bandwidth in last few years. The problem of content-based image retrieval (CBIR of remotely sensed images presents a major challenge not only because of the surprisingly increasing volume of images acquired from a wide range of sensors but also because of the complexity of images themselves. In this paper, a software system for content-based retrieval of remote sensing images using RGB and HSV color spaces is presented. Further, we also compare our results with spatiogram based content retrieval which integrates spatial information along with color histogram. Experimental results show that the integration of spatial information in color improves the image analysis of remote sensing data. In general, retrievals in HSV color space showed better performance than in RGB color space.

Content-based image retrieval applied to bone age assessment

Science.gov (United States)

Fischer, Benedikt; Brosig, André; Welter, Petra; Grouls, Christoph; Günther, Rolf W.; Deserno, Thomas M.

2010-03-01

Radiological bone age assessment is based on local image regions of interest (ROI), such as the epiphysis or the area of carpal bones. These are compared to a standardized reference and scores determining the skeletal maturity are calculated. For computer-aided diagnosis, automatic ROI extraction and analysis is done so far mainly by heuristic approaches. Due to high variations in the imaged biological material and differences in age, gender and ethnic origin, automatic analysis is difficult and frequently requires manual interactions. On the contrary, epiphyseal regions (eROIs) can be compared to previous cases with known age by content-based image retrieval (CBIR). This requires a sufficient number of cases with reliable positioning of the eROI centers. In this first approach to bone age assessment by CBIR, we conduct leaving-oneout experiments on 1,102 left hand radiographs and 15,428 metacarpal and phalangeal eROIs from the USC hand atlas. The similarity of the eROIs is assessed by cross-correlation of 16x16 scaled eROIs. The effects of the number of eROIs, two age computation methods as well as the number of considered CBIR references are analyzed. The best results yield an error rate of 1.16 years and a standard deviation of 0.85 years. As the appearance of the hand varies naturally by up to two years, these results clearly demonstrate the applicability of the CBIR approach for bone age estimation.

W-transform method for feature-oriented multiresolution image retrieval

Energy Technology Data Exchange (ETDEWEB)

Kwong, M.K.; Lin, B. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.

1995-07-01

Image database management is important in the development of multimedia technology. Since an enormous amount of digital images is likely to be generated within the next few decades in order to integrate computers, television, VCR, cables, telephone and various imaging devices. Effective image indexing and retrieval systems are urgently needed so that images can be easily organized, searched, transmitted, and presented. Here, the authors present a local-feature-oriented image indexing and retrieval method based on Kwong, and Tang`s W-transform. Multiresolution histogram comparison is an effective method for content-based image indexing and retrieval. However, most recent approaches perform multiresolution analysis for whole images but do not exploit the local features present in the images. Since W-transform is featured by its ability to handle images of arbitrary size, with no periodicity assumptions, it provides a natural tool for analyzing local image features and building indexing systems based on such features. In this approach, the histograms of the local features of images are used in the indexing, system. The system not only can retrieve images that are similar or identical to the query images but also can retrieve images that contain features specified in the query images, even if the retrieved images as a whole might be very different from the query images. The local-feature-oriented method also provides a speed advantage over the global multiresolution histogram comparison method. The feature-oriented approach is expected to be applicable in managing large-scale image systems such as video databases and medical image databases.

Wave Optics Based LEO-LEO Radio Occultation Retrieval

DEFF Research Database (Denmark)

von Benzon, Hans-Henrik; Høeg, Per

2016-01-01

of the atmospheric products such as the correct water vapor content in the atmosphere. These limitations can be overcome when a proper selected range of high frequency waves are used to probe the atmosphere. Probing frequencies close to the absorption line of water vapor have been included, thus allowing...... the retrieval of the water vapor content. Selecting the correct probing frequencies would make it possible to retrieve other information such as the content of ozone. The retrieval is performed through a number of processing steps which are based on the Full Spectrum Inversion (FSI) technique. The retrieval...... optics based retrieval chain is used on a number of examples and the retrieved atmospheric parameters are compared to the parameters from a global ECMWF analysis model. This model is used in a forward propagator that simulates the electromagnetic field amplitudes and phases at the receiver on board...

Mobile object retrieval in server-based image databases

Science.gov (United States)

Manger, D.; Pagel, F.; Widak, H.

2013-05-01

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

Content-Based Image Retrial Based on Hadoop

Directory of Open Access Journals (Sweden)

DongSheng Yin

2013-01-01

Full Text Available Generally, time complexity of algorithms for content-based image retrial is extremely high. In order to retrieve images on large-scale databases efficiently, a new way for retrieving based on Hadoop distributed framework is proposed. Firstly, a database of images features is built by using Speeded Up Robust Features algorithm and Locality-Sensitive Hashing and then perform the search on Hadoop platform in a parallel way specially designed. Considerable experimental results show that it is able to retrieve images based on content on large-scale cluster and image sets effectively.

Biased discriminant euclidean embedding for content-based image retrieval.

Science.gov (United States)

Bian, Wei; Tao, Dacheng

2010-02-01

With many potential multimedia applications, content-based image retrieval (CBIR) has recently gained more attention for image management and web search. A wide variety of relevance feedback (RF) algorithms have been developed in recent years to improve the performance of CBIR systems. These RF algorithms capture user's preferences and bridge the semantic gap. However, there is still a big room to further the RF performance, because the popular RF algorithms ignore the manifold structure of image low-level visual features. In this paper, we propose the biased discriminative Euclidean embedding (BDEE) which parameterises samples in the original high-dimensional ambient space to discover the intrinsic coordinate of image low-level visual features. BDEE precisely models both the intraclass geometry and interclass discrimination and never meets the undersampled problem. To consider unlabelled samples, a manifold regularization-based item is introduced and combined with BDEE to form the semi-supervised BDEE, or semi-BDEE for short. To justify the effectiveness of the proposed BDEE and semi-BDEE, we compare them against the conventional RF algorithms and show a significant improvement in terms of accuracy and stability based on a subset of the Corel image gallery.

TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

Science.gov (United States)

Li, Shimiao; Gong, Tianxia; Wang, Jie; Liu, Ruizhe; Tan, Chew Lim; Leong, Tze Yun; Pang, Boon Chuan; Lim, C. C. Tchoyoson; Lee, Cheng Kiang; Tian, Qi; Zhang, Zhuo

2010-03-01

Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.

Content-based image retrieval using a signature graph and a self-organizing map

Directory of Open Access Journals (Sweden)

Van Thanh The

2016-06-01

Full Text Available In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL,Wang and MSRDI.

A similarity measure method combining location feature for mammogram retrieval.

Science.gov (United States)

Wang, Zhiqiong; Xin, Junchang; Huang, Yukun; Li, Chen; Xu, Ling; Li, Yang; Zhang, Hao; Gu, Huizi; Qian, Wei

2018-05-28

method have obvious advantage, compared with the content-based image retrieval.

Content-Based Covert Group Detection in Social Networks

Science.gov (United States)

2017-09-06

The students took courses in natural language processing, data mining in various multi-media data sets, text retrieval, text summarization and... mining in social media including: we performed work, on (a) diffusion in social networks, (b) influence maximization in signed social networks, (c...Learning, Information Retrieval, Data Mining and Database. There are 8,293 messages. Our method outperformed state of the art methods based on content

Retrieval Architecture with Classified Query for Content Based Image Recognition

Directory of Open Access Journals (Sweden)

Rik Das

2016-01-01

Full Text Available The consumer behavior has been observed to be largely influenced by image data with increasing familiarity of smart phones and World Wide Web. Traditional technique of browsing through product varieties in the Internet with text keywords has been gradually replaced by the easy accessible image data. The importance of image data has portrayed a steady growth in application orientation for business domain with the advent of different image capturing devices and social media. The paper has described a methodology of feature extraction by image binarization technique for enhancing identification and retrieval of information using content based image recognition. The proposed algorithm was tested on two public datasets, namely, Wang dataset and Oliva and Torralba (OT-Scene dataset with 3688 images on the whole. It has outclassed the state-of-the-art techniques in performance measure and has shown statistical significance.

Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval

Directory of Open Access Journals (Sweden)

Lijuan Duan

2017-01-01

Full Text Available Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI, to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.

Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

Science.gov (United States)

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

2018-06-01

Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

Science.gov (United States)

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

2018-02-01

Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

Combining semantic technologies with a content-based image retrieval system - Preliminary considerations

Science.gov (United States)

Chmiel, P.; Ganzha, M.; Jaworska, T.; Paprzycki, M.

2017-10-01

Nowadays, as a part of systematic growth of volume, and variety, of information that can be found on the Internet, we observe also dramatic increase in sizes of available image collections. There are many ways to help users browsing / selecting images of interest. One of popular approaches are Content-Based Image Retrieval (CBIR) systems, which allow users to search for images that match their interests, expressed in the form of images (query by example). However, we believe that image search and retrieval could take advantage of semantic technologies. We have decided to test this hypothesis. Specifically, on the basis of knowledge captured in the CBIR, we have developed a domain ontology of residential real estate (detached houses, in particular). This allows us to semantically represent each image (and its constitutive architectural elements) represented within the CBIR. The proposed ontology was extended to capture not only the elements resulting from image segmentation, but also "spatial relations" between them. As a result, a new approach to querying the image database (semantic querying) has materialized, thus extending capabilities of the developed system.

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

Science.gov (United States)

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

Context based multimedia information retrieval

DEFF Research Database (Denmark)

Mølgaard, Lasse Lohilahti

The large amounts of digital media becoming available require that new approaches are developed for retrieving, navigating and recommending the data to users in a way that refl ects how we semantically perceive the content. The thesis investigates ways to retrieve and present content for users...... topics from a large collection of the transcribed speech to improve retrieval of spoken documents. The context modelling is done using a variant of probabilistic latent semantic analysis (PLSA), to extract properties of the textual sources that refl ect how humans perceive context. We perform PLSA...... of Wikipedia , as well as text-based semantic similarity. The final aspect investigated is how to include some of the structured data available in Wikipedia to include temporal information. We show that a multiway extension of PLSA makes it possible to extract temporally meaningful topics, better than using...

Advances in estimation methods of vegetation water content based on optical remote sensing techniques

Institute of Scientific and Technical Information of China (English)

无

2010-01-01

Quantitative estimation of vegetation water content(VWC) using optical remote sensing techniques is helpful in forest fire as-sessment,agricultural drought monitoring and crop yield estimation.This paper reviews the research advances of VWC retrieval using spectral reflectance,spectral water index and radiative transfer model(RTM) methods.It also evaluates the reli-ability of VWC estimation using spectral water index from the observation data and the RTM.Focusing on two main definitions of VWC-the fuel moisture content(FMC) and the equivalent water thickness(EWT),the retrieval accuracies of FMC and EWT using vegetation water indices are analyzed.Moreover,the measured information and the dataset are used to estimate VWC,the results show there are significant correlations among three kinds of vegetation water indices(i.e.,WSI,NDⅡ,NDWI1640,WI/NDVI) and canopy FMC of winter wheat(n=45).Finally,the future development directions of VWC detection based on optical remote sensing techniques are also summarized.

Analysis, Retrieval and Delivery of Multimedia Content

CERN Document Server

Cavallaro, Andrea; Leonardi, Riccardo; Migliorati, Pierangelo

2013-01-01

Covering some of the most cutting-edge research on the delivery and retrieval of interactive multimedia content, this volume of specially chosen contributions provides the most updated perspective on one of the hottest contemporary topics. The material represents extended versions of papers presented at the 11th International Workshop on Image Analysis for Multimedia Interactive Services, a vital international forum on this fast-moving field. Logically organized in discrete sections that approach the subject from its various angles, the content deals in turn with content analysis, motion and activity analysis, high-level descriptors and video retrieval, 3-D and multi-view, and multimedia delivery. The chapters cover the finest detail of emerging techniques such as the use of high-level audio information in improving scene segmentation and the use of subjective logic for forensic visual surveillance. On content delivery, the book examines both images and video, focusing on key subjects including an efficient p...

Annotation and retrieval system of CAD models based on functional semantics

Science.gov (United States)

Wang, Zhansong; Tian, Ling; Duan, Wenrui

2014-11-01

CAD model retrieval based on functional semantics is more significant than content-based 3D model retrieval during the mechanical conceptual design phase. However, relevant research is still not fully discussed. Therefore, a functional semantic-based CAD model annotation and retrieval method is proposed to support mechanical conceptual design and design reuse, inspire designer creativity through existing CAD models, shorten design cycle, and reduce costs. Firstly, the CAD model functional semantic ontology is constructed to formally represent the functional semantics of CAD models and describe the mechanical conceptual design space comprehensively and consistently. Secondly, an approach to represent CAD models as attributed adjacency graphs(AAG) is proposed. In this method, the geometry and topology data are extracted from STEP models. On the basis of AAG, the functional semantics of CAD models are annotated semi-automatically by matching CAD models that contain the partial features of which functional semantics have been annotated manually, thereby constructing CAD Model Repository that supports model retrieval based on functional semantics. Thirdly, a CAD model retrieval algorithm that supports multi-function extended retrieval is proposed to explore more potential creative design knowledge in the semantic level. Finally, a prototype system, called Functional Semantic-based CAD Model Annotation and Retrieval System(FSMARS), is implemented. A case demonstrates that FSMARS can successfully botain multiple potential CAD models that conform to the desired function. The proposed research addresses actual needs and presents a new way to acquire CAD models in the mechanical conceptual design phase.

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms

International Nuclear Information System (INIS)

Tourassi, Georgia D.; Harrawood, Brian; Singh, Swatee; Lo, Joseph Y.; Floyd, Carey E.

2007-01-01

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrieval precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses

Applying GA for Optimizing the User Query in Image and Video Retrieval

OpenAIRE

Ehsan Lotfi

2014-01-01

In an information retrieval system, the query can be made by user sketch. The new method presented here, optimizes the user sketch and applies the optimized query to retrieval the information. This optimization may be used in Content-Based Image Retrieval (CBIR) and Content-Based Video Retrieval (CBVR) which is based on trajectory extraction. To optimize the retrieval process, one stage of retrieval is performed by the user sketch. The retrieval criterion is based on the proposed distance met...

Computer-aided diagnostics of screening mammography using content-based image retrieval

Science.gov (United States)

Deserno, Thomas M.; Soiron, Michael; de Oliveira, Júlia E. E.; de A. Araújo, Arnaldo

2012-03-01

Breast cancer is one of the main causes of death among women in occidental countries. In the last years, screening mammography has been established worldwide for early detection of breast cancer, and computer-aided diagnostics (CAD) is being developed to assist physicians reading mammograms. A promising method for CAD is content-based image retrieval (CBIR). Recently, we have developed a classification scheme of suspicious tissue pattern based on the support vector machine (SVM). In this paper, we continue moving towards automatic CAD of screening mammography. The experiments are based on in total 10,509 radiographs that have been collected from different sources. From this, 3,375 images are provided with one and 430 radiographs with more than one chain code annotation of cancerous regions. In different experiments, this data is divided into 12 and 20 classes, distinguishing between four categories of tissue density, three categories of pathology and in the 20 class problem two categories of different types of lesions. Balancing the number of images in each class yields 233 and 45 images remaining in each of the 12 and 20 classes, respectively. Using a two-dimensional principal component analysis, features are extracted from small patches of 128 x 128 pixels and classified by means of a SVM. Overall, the accuracy of the raw classification was 61.6 % and 52.1 % for the 12 and the 20 class problem, respectively. The confusion matrices are assessed for detailed analysis. Furthermore, an implementation of a SVM-based CBIR system for CADx in screening mammography is presented. In conclusion, with a smarter patch extraction, the CBIR approach might reach precision rates that are helpful for the physicians. This, however, needs more comprehensive evaluation on clinical data.

A new method for information retrieval in two-dimensional grating-based X-ray phase contrast imaging

International Nuclear Information System (INIS)

Wang Zhi-Li; Gao Kun; Chen Jian; Ge Xin; Tian Yang-Chao; Wu Zi-Yu; Zhu Pei-Ping

2012-01-01

Grating-based X-ray phase contrast imaging has been demonstrated to be an extremely powerful phase-sensitive imaging technique. By using two-dimensional (2D) gratings, the observable contrast is extended to two refraction directions. Recently, we have developed a novel reverse-projection (RP) method, which is capable of retrieving the object information efficiently with one-dimensional (1D) grating-based phase contrast imaging. In this contribution, we present its extension to the 2D grating-based X-ray phase contrast imaging, named the two-dimensional reverse-projection (2D-RP) method, for information retrieval. The method takes into account the nonlinear contributions of two refraction directions and allows the retrieval of the absorption, the horizontal and the vertical refraction images. The obtained information can be used for the reconstruction of the three-dimensional phase gradient field, and for an improved phase map retrieval and reconstruction. Numerical experiments are carried out, and the results confirm the validity of the 2D-RP method

Robustness of phase retrieval methods in x-ray phase contrast imaging: A comparison

International Nuclear Information System (INIS)

Yan, Aimin; Wu, Xizeng; Liu, Hong

2011-01-01

Purpose: The robustness of the phase retrieval methods is of critical importance for limiting and reducing radiation doses involved in x-ray phase contrast imaging. This work is to compare the robustness of two phase retrieval methods by analyzing the phase maps retrieved from the experimental images of a phantom. Methods: Two phase retrieval methods were compared. One method is based on the transport of intensity equation (TIE) for phase contrast projections, and the TIE-based method is the most commonly used method for phase retrieval in the literature. The other is the recently developed attenuation-partition based (AP-based) phase retrieval method. The authors applied these two methods to experimental projection images of an air-bubble wrap phantom for retrieving the phase map of the bubble wrap. The retrieved phase maps obtained by using the two methods are compared. Results: In the wrap's phase map retrieved by using the TIE-based method, no bubble is recognizable, hence, this method failed completely for phase retrieval from these bubble wrap images. Even with the help of the Tikhonov regularization, the bubbles are still hardly visible and buried in the cluttered background in the retrieved phase map. The retrieved phase values with this method are grossly erroneous. In contrast, in the wrap's phase map retrieved by using the AP-based method, the bubbles are clearly recovered. The retrieved phase values with the AP-based method are reasonably close to the estimate based on the thickness-based measurement. The authors traced these stark performance differences of the two methods to their different techniques employed to deal with the singularity problem involved in the phase retrievals. Conclusions: This comparison shows that the conventional TIE-based phase retrieval method, regardless if Tikhonov regularization is used or not, is unstable against the noise in the wrap's projection images, while the AP-based phase retrieval method is shown in these

Interactive classification and content-based retrieval of tissue images

Science.gov (United States)

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

2002-11-01

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

Algorithm for image retrieval based on edge gradient orientation statistical code.

Science.gov (United States)

Zeng, Jiexian; Zhao, Yonggang; Li, Weiye; Fu, Xiang

2014-01-01

Image edge gradient direction not only contains important information of the shape, but also has a simple, lower complexity characteristic. Considering that the edge gradient direction histograms and edge direction autocorrelogram do not have the rotation invariance, we put forward the image retrieval algorithm which is based on edge gradient orientation statistical code (hereinafter referred to as EGOSC) by sharing the application of the statistics method in the edge direction of the chain code in eight neighborhoods to the statistics of the edge gradient direction. Firstly, we construct the n-direction vector and make maximal summation restriction on EGOSC to make sure this algorithm is invariable for rotation effectively. Then, we use Euclidean distance of edge gradient direction entropy to measure shape similarity, so that this method is not sensitive to scaling, color, and illumination change. The experimental results and the algorithm analysis demonstrate that the algorithm can be used for content-based image retrieval and has good retrieval results.

Interactive Exploration for Image Retrieval

Directory of Open Access Journals (Sweden)

Jérôme Fournier

2005-08-01

Full Text Available We present a new version of our content-based image retrieval system RETIN. It is based on adaptive quantization of the color space, together with new features aiming at representing the spatial relationship between colors. Color analysis is also extended to texture. Using these powerful indexes, an original interactive retrieval strategy is introduced. The process is based on two steps for handling the retrieval of very large image categories. First, a controlled exploration method of the database is presented. Second, a relevance feedback method based on statistical learning is proposed. All the steps are evaluated by experiments on a generalist database.

Enhancing Image Retrieval System Using Content Based Search ...

African Journals Online (AJOL)

The output shows more efficiency in retrieval because instead of performing the search on the entire image database, the image category option directs the retrieval engine to the specified category. Also, there is provision to update or modify the different image categories in the image database as need arise. Keywords: ...

A multi-tiered architecture for content retrieval in mobile peer-to-peer networks.

Science.gov (United States)

2012-01-01

In this paper, we address content retrieval in Mobile Peer-to-Peer (P2P) Networks. We design a multi-tiered architecture for content : retrieval, where at Tier 1, we design a protocol for content similarity governed by a parameter that trades accu...

Pareto-depth for multiple-query image retrieval.

Science.gov (United States)

Hsiao, Ko-Jen; Calder, Jeff; Hero, Alfred O

2015-02-01

Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper, we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a novel multiple-query information retrieval algorithm that combines the Pareto front method with efficient manifold ranking. We show that our proposed algorithm outperforms state of the art multiple-query retrieval algorithms on real-world image databases. We attribute this performance improvement to concavity properties of the Pareto fronts, and prove a theoretical result that characterizes the asymptotic concavity of the fronts.

High sensitivity phase retrieval method in grating-based x-ray phase contrast imaging

Energy Technology Data Exchange (ETDEWEB)

Wu, Zhao; Gao, Kun; Chen, Jian; Wang, Dajiang; Wang, Shenghao; Chen, Heng; Bao, Yuan; Shao, Qigang; Wang, Zhili, E-mail: wangnsrl@ustc.edu.cn [National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230029 (China); Zhang, Kai [Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049 (China); Zhu, Peiping; Wu, Ziyu, E-mail: wuzy@ustc.edu.cn [National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230029, China and Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049 (China)

2015-02-15

Purpose: Grating-based x-ray phase contrast imaging is considered as one of the most promising techniques for future medical imaging. Many different methods have been developed to retrieve phase signal, among which the phase stepping (PS) method is widely used. However, further practical implementations are hindered, due to its complex scanning mode and high radiation dose. In contrast, the reverse projection (RP) method is a novel fast and low dose extraction approach. In this contribution, the authors present a quantitative analysis of the noise properties of the refraction signals retrieved by the two methods and compare their sensitivities. Methods: Using the error propagation formula, the authors analyze theoretically the signal-to-noise ratios (SNRs) of the refraction images retrieved by the two methods. Then, the sensitivities of the two extraction methods are compared under an identical exposure dose. Numerical experiments are performed to validate the theoretical results and provide some quantitative insight. Results: The SNRs of the two methods are both dependent on the system parameters, but in different ways. Comparison between their sensitivities reveals that for the refraction signal, the RP method possesses a higher sensitivity, especially in the case of high visibility and/or at the edge of the object. Conclusions: Compared with the PS method, the RP method has a superior sensitivity and provides refraction images with a higher SNR. Therefore, one can obtain highly sensitive refraction images in grating-based phase contrast imaging. This is very important for future preclinical and clinical implementations.

High sensitivity phase retrieval method in grating-based x-ray phase contrast imaging

International Nuclear Information System (INIS)

Wu, Zhao; Gao, Kun; Chen, Jian; Wang, Dajiang; Wang, Shenghao; Chen, Heng; Bao, Yuan; Shao, Qigang; Wang, Zhili; Zhang, Kai; Zhu, Peiping; Wu, Ziyu

2015-01-01

Purpose: Grating-based x-ray phase contrast imaging is considered as one of the most promising techniques for future medical imaging. Many different methods have been developed to retrieve phase signal, among which the phase stepping (PS) method is widely used. However, further practical implementations are hindered, due to its complex scanning mode and high radiation dose. In contrast, the reverse projection (RP) method is a novel fast and low dose extraction approach. In this contribution, the authors present a quantitative analysis of the noise properties of the refraction signals retrieved by the two methods and compare their sensitivities. Methods: Using the error propagation formula, the authors analyze theoretically the signal-to-noise ratios (SNRs) of the refraction images retrieved by the two methods. Then, the sensitivities of the two extraction methods are compared under an identical exposure dose. Numerical experiments are performed to validate the theoretical results and provide some quantitative insight. Results: The SNRs of the two methods are both dependent on the system parameters, but in different ways. Comparison between their sensitivities reveals that for the refraction signal, the RP method possesses a higher sensitivity, especially in the case of high visibility and/or at the edge of the object. Conclusions: Compared with the PS method, the RP method has a superior sensitivity and provides refraction images with a higher SNR. Therefore, one can obtain highly sensitive refraction images in grating-based phase contrast imaging. This is very important for future preclinical and clinical implementations

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

Science.gov (United States)

Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

2018-01-01

Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie

A diagram retrieval method with multi-label learning

Science.gov (United States)

Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

2015-01-01

In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

Experiments with a novel content-based image retrieval software: can we eliminate classification systems in adolescent idiopathic scoliosis?

Science.gov (United States)

Menon, K Venugopal; Kumar, Dinesh; Thomas, Tessamma

2014-02-01

Study Design Preliminary evaluation of new tool. Objective To ascertain whether the newly developed content-based image retrieval (CBIR) software can be used successfully to retrieve images of similar cases of adolescent idiopathic scoliosis (AIS) from a database to help plan treatment without adhering to a classification scheme. Methods Sixty-two operated cases of AIS were entered into the newly developed CBIR database. Five new cases of different curve patterns were used as query images. The images were fed into the CBIR database that retrieved similar images from the existing cases. These were analyzed by a senior surgeon for conformity to the query image. Results Within the limits of variability set for the query system, all the resultant images conformed to the query image. One case had no similar match in the series. The other four retrieved several images that were matching with the query. No matching case was left out in the series. The postoperative images were then analyzed to check for surgical strategies. Broad guidelines for treatment could be derived from the results. More precise query settings, inclusion of bending films, and a larger database will enhance accurate retrieval and better decision making. Conclusion The CBIR system is an effective tool for accurate documentation and retrieval of scoliosis images. Broad guidelines for surgical strategies can be made from the postoperative images of the existing cases without adhering to any classification scheme.

Image Retrieval based on Integration between Color and Geometric Moment Features

International Nuclear Information System (INIS)

Saad, M.H.; Saleh, H.I.; Konbor, H.; Ashour, M.

2012-01-01

Content based image retrieval is the retrieval of images based on visual features such as colour, texture and shape. .the Current approaches to CBIR differ in terms of which image features are extracted; recent work deals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. content-based image retrieval has many application areas such as, education, commerce, military, searching, commerce, and biomedicine and Web image classification. This paper proposes a new image retrieval system, which uses color and geometric moment feature to form the feature vectors. Bhattacharyya distance and histogram intersection are used to perform feature matching. This framework integrates the color histogram which represents the global feature and geometric moment as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard the results shows that a combination of our approach as a local image descriptor with other global descriptors outperforms other approaches.

Optimization of reference library used in content-based medical image retrieval scheme

International Nuclear Information System (INIS)

Park, Sang Cheol; Sukthankar, Rahul; Mummert, Lily; Satyanarayanan, Mahadev; Zheng Bin

2007-01-01

Building an optimal image reference library is a critical step in developing the interactive computer-aided detection and diagnosis (I-CAD) systems of medical images using content-based image retrieval (CBIR) schemes. In this study, the authors conducted two experiments to investigate (1) the relationship between I-CAD performance and size of reference library and (2) a new reference selection strategy to optimize the library and improve I-CAD performance. The authors assembled a reference library that includes 3153 regions of interest (ROI) depicting either malignant masses (1592) or CAD-cued false-positive regions (1561) and an independent testing data set including 200 masses and 200 false-positive regions. A CBIR scheme using a distance-weighted K-nearest neighbor algorithm is applied to retrieve references that are considered similar to the testing sample from the library. The area under receiver operating characteristic curve (A z ) is used as an index to evaluate the I-CAD performance. In the first experiment, the authors systematically increased reference library size and tested I-CAD performance. The result indicates that scheme performance improves initially from A z =0.715 to 0.874 and then plateaus when the library size reaches approximately half of its maximum capacity. In the second experiment, based on the hypothesis that a ROI should be removed if it performs poorly compared to a group of similar ROIs in a large and diverse reference library, the authors applied a new strategy to identify 'poorly effective' references. By removing 174 identified ROIs from the reference library, I-CAD performance significantly increases to A z =0.914 (p<0.01). The study demonstrates that increasing reference library size and removing poorly effective references can significantly improve I-CAD performance

Complex event processing for content-based text, image, and video retrieval

NARCIS (Netherlands)

Bowman, E.K.; Broome, B.D.; Holland, V.M.; Summers-Stay, D.; Rao, R.M.; Duselis, J.; Howe, J.; Madahar, B.K.; Boury-Brisset, A.C.; Forrester, B.; Kwantes, P.; Burghouts, G.; Huis, J. van; Mulayim, A.Y.

2016-01-01

This report summarizes the findings of an exploratory team of the North Atlantic Treaty Organization (NATO) Information Systems Technology panel into Content-Based Analytics (CBA). The team carried out a technical review into the current status of theoretical and practical developments of methods,

[Vegetation index estimation by chlorophyll content of grassland based on spectral analysis].

Science.gov (United States)

Xiao, Han; Chen, Xiu-Wan; Yang, Zhen-Yu; Li, Huai-Yu; Zhu, Han

2014-11-01

Comparing the methods of existing remote sensing research on the estimation of chlorophyll content, the present paper confirms that the vegetation index is one of the most practical and popular research methods. In recent years, the increasingly serious problem of grassland degradation. This paper, firstly, analyzes the measured reflectance spectral curve and its first derivative curve in the grasslands of Songpan, Sichuan and Gongger, Inner Mongolia, conducts correlation analysis between these two spectral curves and chlorophyll content, and finds out the regulation between REP (red edge position) and grassland chlorophyll content, that is, the higher the chlorophyll content is, the higher the REIP (red-edge inflection point) value would be. Then, this paper constructs GCI (grassland chlorophyll index) and selects the most suitable band for retrieval. Finally, this paper calculates the GCI by the use of satellite hyperspectral image, conducts the verification and accuracy analysis of the calculation results compared with chlorophyll content data collected from field of twice experiments. The result shows that for grassland chlorophyll content, GCI has stronger sensitivity than other indices of chlorophyll, and has higher estimation accuracy. GCI is the first proposed to estimate the grassland chlorophyll content, and has wide application potential for the remote sensing retrieval of grassland chlorophyll content. In addition, the grassland chlorophyll content estimation method based on remote sensing retrieval in this paper provides new research ideas for other vegetation biochemical parameters' estimation, vegetation growth status' evaluation and grassland ecological environment change's monitoring.

iPixel: a visual content-based and semantic search engine for retrieving digitized mammograms by using collective intelligence.

Science.gov (United States)

Alor-Hernández, Giner; Pérez-Gallardo, Yuliana; Posada-Gómez, Rubén; Cortes-Robles, Guillermo; Rodríguez-González, Alejandro; Aguilar-Laserre, Alberto A

2012-09-01

Nowadays, traditional search engines such as Google, Yahoo and Bing facilitate the retrieval of information in the format of images, but the results are not always useful for the users. This is mainly due to two problems: (1) the semantic keywords are not taken into consideration and (2) it is not always possible to establish a query using the image features. This issue has been covered in different domains in order to develop content-based image retrieval (CBIR) systems. The expert community has focussed their attention on the healthcare domain, where a lot of visual information for medical analysis is available. This paper provides a solution called iPixel Visual Search Engine, which involves semantics and content issues in order to search for digitized mammograms. iPixel offers the possibility of retrieving mammogram features using collective intelligence and implementing a CBIR algorithm. Our proposal compares not only features with similar semantic meaning, but also visual features. In this sense, the comparisons are made in different ways: by the number of regions per image, by maximum and minimum size of regions per image and by average intensity level of each region. iPixel Visual Search Engine supports the medical community in differential diagnoses related to the diseases of the breast. The iPixel Visual Search Engine has been validated by experts in the healthcare domain, such as radiologists, in addition to experts in digital image analysis.

A computational approach to content-based retrieval of folk song melodies

NARCIS (Netherlands)

van Kranenburg, P.

2010-01-01

In order to develop a Music Information Retrieval system for folksong melodies, one needs to design an adequate computational model of melodic similarity, which is the subject of this Ph.D. thesis. Since understanding of both the properties of the melodies and computational methods is necessary,

Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval

International Nuclear Information System (INIS)

Xu Jiajing; Napel, Sandy; Greenspan, Hayit; Beaulieu, Christopher F.; Agrawal, Neeraj; Rubin, Daniel

2012-01-01

. Equivalence across deformations was assessed using Schuirmann's paired two one-sided tests. Results: In simulated images, the concordance correlation between measured gradient and actual gradient was 0.994. The mean (s.d.) and standard deviation NDCG score for the retrieval of K images, K = 5, 10, and 15, were 84% (8%), 85% (7%), and 85% (7%) for CT images containing liver lesions, and 82% (7%), 84% (6%), and 85% (4%) for CT images containing lung nodules, respectively. The authors’ proposed method outperformed the two existing margin characterization methods in average NDCG scores over all K, by 1.5% and 3% in datasets containing liver lesion, and 4.5% and 5% in datasets containing lung nodules. Equivalence testing showed that the authors’ feature is more robust across all margin deformations (p < 0.05) than the two existing methods for margin sharpness characterization in both simulated and clinical datasets. Conclusions: The authors have described a new image feature to quantify the margin sharpness of lesions. It has strong correlation with known margin sharpness in simulated images and in clinical CT images containing liver lesions and lung nodules. This image feature has excellent performance for retrieving images with similar margin characteristics, suggesting potential utility, in conjunction with other lesion features, for content-based image retrieval applications.

Understanding human quality judgment in assessing online forum contents for thread retrieval purpose

Science.gov (United States)

Ismail, Zuriati; Salim, Naomie; Huspi, Sharin Hazlin

2017-10-01

Compared to traditional materials or journals, user-generated contents are not peer-reviewed. Lack of quality control and the explosive growth of web contents make the task of finding quality information on the web especially critical. The existence of new facilities for producing web contents such as forum makes this issue more significant. This study focuses on online forums threads or discussion, where the forums contain valuable human-generated information in a form of discussions. Due to the unique structure of the online forum pages, special techniques are required to organize and search for information in these forums. Quality biased retrieval is a retrieval approach that search for relevant document and prioritized higher quality documents. Despite major concern of quality content and recent development of quality biased retrieval, there is an urgent need to understand how quality content is being judged, for retrieval and performance evaluation purposes. Furthermore, even though there are various studies on the quality of information, there is no standard framework that has been established. The primary aim of this paper is to contribute to the understanding of human quality judgment in assessing online forum contents. The foundation of this study is to compare and evaluate different frameworks (for quality biased retrieval and information quality). This led to the finding that many quality dimensions are redundant and some dimensions are understood differently between different studies. We conducted a survey on crowdsourcing community to measure the importance of each quality dimensions found in various frameworks. Accuracy and ease of understanding are among top important dimensions while threads popularity and contents manipulability are among least important dimensions. This finding is beneficial in evaluating contents of online forum.

Content-Based Information Retrieval from Forensic Databases

NARCIS (Netherlands)

Geradts, Z.J.M.H.

2002-01-01

In forensic science, the number of image databases is growing rapidly. For this reason, it is necessary to have a proper procedure for searching in these images databases based on content. The use of image databases results in more solved crimes; furthermore, statistical information can be obtained

Evolutionary Computing Methods for Spectral Retrieval

Science.gov (United States)

Terrile, Richard; Fink, Wolfgang; Huntsberger, Terrance; Lee, Seugwon; Tisdale, Edwin; VonAllmen, Paul; Tinetti, Geivanna

2009-01-01

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment

International Nuclear Information System (INIS)

Wang Xiaohui; Park, Sang Cheol; Zheng Bin

2009-01-01

This study aims to assess three methods commonly used in content-based image retrieval (CBIR) schemes and investigate the approaches to improve scheme performance. A reference database involving 3000 regions of interest (ROIs) was established. Among them, 400 ROIs were randomly selected to form a testing dataset. Three methods, namely mutual information, Pearson's correlation and a multi-feature-based k-nearest neighbor (KNN) algorithm, were applied to search for the 15 'the most similar' reference ROIs to each testing ROI. The clinical relevance and visual similarity of searching results were evaluated using the areas under receiver operating characteristic (ROC) curves (A Z ) and average mean square difference (MSD) of the mass boundary spiculation level ratings between testing and selected ROIs, respectively. The results showed that the A Z values were 0.893 ± 0.009, 0.606 ± 0.021 and 0.699 ± 0.026 for the use of KNN, mutual information and Pearson's correlation, respectively. The A Z values increased to 0.724 ± 0.017 and 0.787 ± 0.016 for mutual information and Pearson's correlation when using ROIs with the size adaptively adjusted based on actual mass size. The corresponding MSD values were 2.107 ± 0.718, 2.301 ± 0.733 and 2.298 ± 0.743. The study demonstrates that due to the diversity of medical images, CBIR schemes using multiple image features and mass size-based ROIs can achieve significantly improved performance.

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

Directory of Open Access Journals (Sweden)

Yansheng Li

2016-08-01

Full Text Available With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF. In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP, gray level co-occurrence (GLCM, maximal response 8 (MR8, and scale-invariant feature transform (SIFT. In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM dataset and the Wuhan University (WH dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.

Content-based analysis and indexing of sports video

Science.gov (United States)

Luo, Ming; Bai, Xuesheng; Xu, Guang-you

2001-12-01

An explosion of on-line image and video data in digital form is already well underway. With the exponential rise in interactive information exploration and dissemination through the World-Wide Web, the major inhibitors of rapid access to on-line video data are the management of capture and storage, and content-based intelligent search and indexing techniques. This paper proposes an approach for content-based analysis and event-based indexing of sports video. It includes a novel method to organize shots - classifying shots as close shots and far shots, an original idea of blur extent-based event detection, and an innovative local mutation-based algorithm for caption detection and retrieval. Results on extensive real TV programs demonstrate the applicability of our approach.

Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

Science.gov (United States)

Ko, Byoung Chul; Lee, JiHyeon; Nam, Jae-Yeal

2012-08-01

This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.

Linear information retrieval method in X-ray grating-based phase contrast imaging and its interchangeability with tomographic reconstruction

Science.gov (United States)

Wu, Z.; Gao, K.; Wang, Z. L.; Shao, Q. G.; Hu, R. F.; Wei, C. X.; Zan, G. B.; Wali, F.; Luo, R. H.; Zhu, P. P.; Tian, Y. C.

2017-06-01

In X-ray grating-based phase contrast imaging, information retrieval is necessary for quantitative research, especially for phase tomography. However, numerous and repetitive processes have to be performed for tomographic reconstruction. In this paper, we report a novel information retrieval method, which enables retrieving phase and absorption information by means of a linear combination of two mutually conjugate images. Thanks to the distributive law of the multiplication as well as the commutative law and associative law of the addition, the information retrieval can be performed after tomographic reconstruction, thus simplifying the information retrieval procedure dramatically. The theoretical model of this method is established in both parallel beam geometry for Talbot interferometer and fan beam geometry for Talbot-Lau interferometer. Numerical experiments are also performed to confirm the feasibility and validity of the proposed method. In addition, we discuss its possibility in cone beam geometry and its advantages compared with other methods. Moreover, this method can also be employed in other differential phase contrast imaging methods, such as diffraction enhanced imaging, non-interferometric imaging, and edge illumination.

PROTOTYPE CONTENT BASED IMAGE RETRIEVAL UNTUK DETEKSI PEN YAKIT KULIT DENGAN METODE EDGE DETECTION

Directory of Open Access Journals (Sweden)

Erick Fernando

2016-05-01

Full Text Available Dokter spesialis kulit melakukan pemeriksa secara visual objek mata, capture objek dengan kamera digital dan menanyakan riwayat perjalanan penyakit pasien, tanpa melakukan perbandingan terhadap gejala dan tanda yang ada sebelummnya. Sehingga pemeriksaan dan perkiraan jenis penyakit kulit. Pengolahan data citra dalam bentuk digital khususnya citra medis sudah sangat dibutuhkan dengan pra-processing. Banyak pasien yang dilayani di rumah sakit masih menggunakan data citra analog. Data analog ini membutuhkan ruangan khusus untuk menyimpan guna menghindarkan kerusakan mekanis. Uraian mengatasi permasalahan ini, citra medis dibuat dalam bentuk digital dan disimpan dalam sistem database dan dapat melihat kesamaan citra kulit yang baru. Citra akan dapat ditampilkan dengan pra- processing dengan identifikasi kesamaan dengan Content Based Image Retrieval (CBIR bekerja dengan cara mengukur kemiripan citra query dengan semua citra yang ada dalam database sehingga query cost berbanding lurus dengan jumlah citra dalam database.

Design Pattern Retrieval and Style Analysis for Content Creation of Comic Figures

Directory of Open Access Journals (Sweden)

Bor-Shen Lin

2014-01-01

Full Text Available Placement of objects within a constrained space is a common challenge for designers; it is associated with decisions regarding the furnishing of a space with furniture, collocation of dressings, flower arrangement, and design of comic figures. Though many design elements can be shared on the Internet in the current age of technology, it is still not easy to compare or search for design patterns based on these elements. Thus, it is difficult for designers to efficiently retrieve similar patterns designed by others, to compare them, or to learn from them. This paper proposes the architecture of representing, comparing, retrieving, and analyzing the design patterns of digital contents for design support. This scheme can help the designers to explore the huge space of design patterns efficiently, to analyze and summarize the design styles quickly, and to improve design skills and stimulate imaginations effectively during the process of learning or creating. The proposed scheme has been verified with a design support system for the content creation of comic figures. It is generally applicable to the creation of digital contents and shows potential for applications in the fields of design and education.

PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

Science.gov (United States)

Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

2004-11-01

Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

Dialog-based Interactive Image Retrieval

OpenAIRE

Guo, Xiaoxiao; Wu, Hui; Cheng, Yu; Rennie, Steven; Feris, Rogerio Schmidt

2018-01-01

Existing methods for interactive image retrieval have demonstrated the merit of integrating user feedback, improving retrieval results. However, most current systems rely on restricted forms of user feedback, such as binary relevance responses, or feedback based on a fixed set of relative attributes, which limits their impact. In this paper, we introduce a new approach to interactive image search that enables users to provide feedback via natural language, allowing for more natural and effect...

Content Based Searching for INIS

International Nuclear Information System (INIS)

Jain, V.; Jain, R.K.

2016-01-01

Full text: Whatever a user wants is available on the internet, but to retrieve the information efficiently, a multilingual and most-relevant document search engine is a must. Most current search engines are word based or pattern based. They do not consider the meaning of the query posed to them; purely based on the keywords of the query; no support of multilingual query and and dismissal of nonrelevant results. Current information-retrieval techniques either rely on an encoding process, using a certain perspective or classification scheme, to describe a given item, or perform a full-text analysis, searching for user-specified words. Neither case guarantees content matching because an encoded description might reflect only part of the content and the mere occurrence of a word does not necessarily reflect the document’s content. For general documents, there doesn’t yet seem to be a much better option than lazy full-text analysis, by manually going through those endless results pages. In contrast to this, new search engine should extract the meaning of the query and then perform the search based on this extracted meaning. New search engine should also employ Interlingua based machine translation technology to present information in the language of choice of the user. (author

Multimedia human brain database system for surgical candidacy determination in temporal lobe epilepsy with content-based image retrieval

Science.gov (United States)

Siadat, Mohammad-Reza; Soltanian-Zadeh, Hamid; Fotouhi, Farshad A.; Elisevich, Kost

2003-01-01

This paper presents the development of a human brain multimedia database for surgical candidacy determination in temporal lobe epilepsy. The focus of the paper is on content-based image management, navigation and retrieval. Several medical image-processing methods including our newly developed segmentation method are utilized for information extraction/correlation and indexing. The input data includes T1-, T2-Weighted MRI and FLAIR MRI and ictal and interictal SPECT modalities with associated clinical data and EEG data analysis. The database can answer queries regarding issues such as the correlation between the attribute X of the entity Y and the outcome of a temporal lobe epilepsy surgery. The entity Y can be a brain anatomical structure such as the hippocampus. The attribute X can be either a functionality feature of the anatomical structure Y, calculated with SPECT modalities, such as signal average, or a volumetric/morphological feature of the entity Y such as volume or average curvature. The outcome of the surgery can be any surgery assessment such as memory quotient. A determination is made regarding surgical candidacy by analysis of both textual and image data. The current database system suggests a surgical determination for the cases with relatively small hippocampus and high signal intensity average on FLAIR images within the hippocampus. This indication pretty much fits with the surgeons" expectations/observations. Moreover, as the database gets more populated with patient profiles and individual surgical outcomes, using data mining methods one may discover partially invisible correlations between the contents of different modalities of data and the outcome of the surgery.

Robust retrieval of fine art paintings

Science.gov (United States)

Smolka, Bogdan; Lukac, Rastislav; Plataniotis, Konstantinos N.; Venetsanopoulos, Anastasios N.

2003-10-01

The rapid growth of image archives increases the need for efficient and fast tools that can retrieve and search through large amount of visual data. In this paper we propose an efficient method of extracting the image color content, which serves as an image digital signature, allowing to efficiently index and retrieve the content of large, heterogeneous multimedia databases. We apply the proposed method for the retrieval of images from the WEBMUSEUM Internet database, containing the collection of fine art images and show that the new method of image color representation is robust to image distorsions caused by resizing and compression and can be incorporated into existing retrieval systems which exploit the information on color content in digital images.

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations. Part I: Forward Model, Error Analysis, and Information Content

Science.gov (United States)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (tau), effective radius (r(sub eff)), and cloud top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary data sets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations. Part I: Forward model, error analysis, and information content

Science.gov (United States)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2018-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (τ), effective radius (reff), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available. PMID:29707470

Research of image retrieval technology based on color feature

Science.gov (United States)

Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

2009-10-01

Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram

Biogas slurry pricing method based on nutrient content

Science.gov (United States)

Zhang, Chang-ai; Guo, Honghai; Yang, Zhengtao; Xin, Shurong

2017-11-01

In order to promote biogas-slurry commercialization, A method was put forward to valuate biogas slurry based on its nutrient contents. Firstly, element contents of biogas slurry was measured; Secondly, each element was valuated based on its market price, and then traffic cost, using cost and market effect were taken into account, the pricing method of biogas slurry were obtained lastly. This method could be useful in practical production. Taking cattle manure raw meterial biogas slurry and con stalk raw material biogas slurry for example, their price were 38.50 yuan RMB per ton and 28.80 yuan RMB per ton. This paper will be useful for recognizing the value of biogas projects, ensuring biogas project running, and instructing the cyclic utilization of biomass resources in China.

Multimedia information retrieval theory and techniques

CERN Document Server

Raieli, Roberto

2013-01-01

Novel processing and searching tools for the management of new multimedia documents have developed. Multimedia Information Retrieval (MMIR) is an organic system made up of Text Retrieval (TR); Visual Retrieval (VR); Video Retrieval (VDR); and Audio Retrieval (AR) systems. So that each type of digital document may be analysed and searched by the elements of language appropriate to its nature, search criteria must be extended. Such an approach is known as the Content Based Information Retrieval (CBIR), and is the core of MMIR. This novel content-based concept of information handling needs to be integrated with more traditional semantics. Multimedia Information Retrieval focuses on the tools of processing and searching applicable to the content-based management of new multimedia documents. Translated from Italian by Giles Smith, the book is divided in to two parts. Part one discusses MMIR and related theories, and puts forward new methodologies; part two reviews various experimental and operating MMIR systems, a...

Efficient data retrieval method for similar plasma waveforms in EAST

Energy Technology Data Exchange (ETDEWEB)

Liu, Ying, E-mail: liuying-ipp@szu.edu.cn [SZU-CASIPP Joint Laboratory for Applied Plasma, Shenzhen University, Shenzhen 518060 (China); Huang, Jianjun; Zhou, Huasheng; Wang, Fan [SZU-CASIPP Joint Laboratory for Applied Plasma, Shenzhen University, Shenzhen 518060 (China); Wang, Feng [Institute of Plasma Physics Chinese Academy of Sciences, Hefei 230031 (China)

2016-11-15

Highlights: • The proposed method is carried out by means of bounding envelope and angle distance. • It allows retrieving for whole similar waveforms of any time length. • In addition, the proposed method is also possible to retrieve subsequences. - Abstract: Fusion research relies highly on data analysis due to its massive-sized database. In the present work, we propose an efficient method for searching and retrieving similar plasma waveforms in Experimental Advanced Superconducting Tokamak (EAST). Based on Piecewise Linear Aggregate Approximation (PLAA) for extracting feature values, the searching process is accomplished in two steps. The first one is coarse searching to narrow down the search space, which is carried out by means of bounding envelope. The second step is fine searching to retrieval similar waveforms, which is implemented by the angle distance. The proposed method is tested in EAST databases and turns out to have good performance in retrieving similar waveforms.

Video retrieval by still-image analysis with ImageMiner

Science.gov (United States)

Kreyss, Jutta; Roeper, M.; Alshuth, Peter; Hermes, Thorsten; Herzog, Otthein

1997-01-01

The large amount of available multimedia information (e.g. videos, audio, images) requires efficient and effective annotation and retrieval methods. As videos start playing a more important role in the frame of multimedia, we want to make these available for content-based retrieval. The ImageMiner-System, which was developed at the University of Bremen in the AI group, is designed for content-based retrieval of single images by a new combination of techniques and methods from computer vision and artificial intelligence. In our approach to make videos available for retrieval in a large database of videos and images there are two necessary steps: First, the detection and extraction of shots from a video, which is done by a histogram based method and second, the construction of the separate frames in a shot to one still single images. This is performed by a mosaicing-technique. The resulting mosaiced image gives a one image visualization of the shot and can be analyzed by the ImageMiner-System. ImageMiner has been tested on several domains, (e.g. landscape images, technical drawings), which cover a wide range of applications.

Region-Based Color Image Indexing and Retrieval

DEFF Research Database (Denmark)

Kompatsiaris, Ioannis; Triantafyllou, Evangelia; Strintzis, Michael G.

2001-01-01

In this paper a region-based color image indexing and retrieval algorithm is presented. As a basis for the indexing, a novel K-Means segmentation algorithm is used, modified so as to take into account the coherence of the regions. A new color distance is also defined for this algorithm. Based on ....... Experimental results demonstrate the performance of the algorithm. The development of an intelligent image content-based search engine for the World Wide Web is also presented, as a direct application of the presented algorithm....

Beyond information retrieval: information discovery and multimedia information retrieval

OpenAIRE

Roberto Raieli

2017-01-01

The paper compares the current methodologies for search and discovery of information and information resources: terminological search and term-based language, own of information retrieval (IR); semantic search and information discovery, being developed mainly through the language of linked data; semiotic search and content-based language, experienced by multimedia information retrieval (MIR).MIR semiotic methodology is, then, detailed.

SIFT Meets CNN: A Decade Survey of Instance Retrieval.

Science.gov (United States)

Zheng, Liang; Yang, Yi; Tian, Qi

2018-05-01

In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.

AN ENSEMBLE TEMPLATE MATCHING AND CONTENT-BASED IMAGE RETRIEVAL SCHEME TOWARDS EARLY STAGE DETECTION OF MELANOMA

Directory of Open Access Journals (Sweden)

Spiros Kostopoulos

2016-12-01

Full Text Available Malignant melanoma represents the most dangerous type of skin cancer. In this study we present an ensemble classification scheme, employing the mutual information, the cross-correlation and the clustering based on proximity of image features methods, for early stage assessment of melanomas on plain photography images. The proposed scheme performs two main operations. First, it retrieves the most similar, to the unknown case, image samples from an available image database with verified benign moles and malignant melanoma cases. Second, it provides an automated estimation regarding the nature of the unknown image sample based on the majority of the most similar images retrieved from the available database. Clinical material comprised 75 melanoma and 75 benign plain photography images collected from publicly available dermatological atlases. Results showed that the ensemble scheme outperformed all other methods tested in terms of accuracy with 94.9±1.5%, following an external cross-validation evaluation methodology. The proposed scheme may benefit patients by providing a second opinion consultation during the self-skin examination process and the physician by providing a second opinion estimation regarding the nature of suspicious moles that may assist towards decision making especially for ambiguous cases, safeguarding, in this way from potential diagnostic misinterpretations.

Information content of OCO-2 oxygen A-band channels for retrieving marine liquid cloud properties

Science.gov (United States)

Richardson, Mark; Stephens, Graeme L.

2018-03-01

Information content analysis is used to select channels for a marine liquid cloud retrieval using the high-spectral-resolution oxygen A-band instrument on NASA's Orbiting Carbon Observatory-2 (OCO-2). Desired retrieval properties are cloud optical depth, cloud-top pressure and cloud pressure thickness, which is the geometric thickness expressed in hectopascals. Based on information content criteria we select a micro-window of 75 of the 853 functioning OCO-2 channels spanning 763.5-764.6 nm and perform a series of synthetic retrievals with perturbed initial conditions. We estimate posterior errors from the sample standard deviations and obtain ±0.75 in optical depth and ±12.9 hPa in both cloud-top pressure and cloud pressure thickness, although removing the 10 % of samples with the highest χ2 reduces posterior error in cloud-top pressure to ±2.9 hPa and cloud pressure thickness to ±2.5 hPa. The application of this retrieval to real OCO-2 measurements is briefly discussed, along with limitations and the greatest caution is urged regarding the assumption of a single homogeneous cloud layer, which is often, but not always, a reasonable approximation for marine boundary layer clouds.

Color-Based Image Retrieval from High-Similarity Image Databases

DEFF Research Database (Denmark)

Hansen, Michael Adsetts Edberg; Carstensen, Jens Michael

2003-01-01

Many image classification problems can fruitfully be thought of as image retrieval in a "high similarity image database" (HSID) characterized by being tuned towards a specific application and having a high degree of visual similarity between entries that should be distinguished. We introduce...... a method for HSID retrieval using a similarity measure based on a linear combination of Jeffreys-Matusita (JM) distances between distributions of color (and color derivatives) estimated from a set of automatically extracted image regions. The weight coefficients are estimated based on optimal retrieval...... performance. Experimental results on the difficult task of visually identifying clones of fungal colonies grown in a petri dish and categorization of pelts show a high retrieval accuracy of the method when combined with standardized sample preparation and image acquisition....

Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval

Directory of Open Access Journals (Sweden)

Muhammad Imran

2014-01-01

Full Text Available One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF coupled with support vector machine (SVM has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO. The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

Test of a method for phase retrieval in HRTEM

International Nuclear Information System (INIS)

Beeching, M.J.; Spargo, A.E.C.

1992-01-01

Several methods of phase retrieval from the high resolution transmission electron microscopy image intensity distributions have been proposed but no standard method has emerged. The results achieved using a technique based on a development of the focus variation method, are reported. The dependence of the retrieved wavefunction on various experimental parameters is investigated and the method tested using computer simulated images. The method is shown to be useful in finding the complex wavefield at the exit surface of the specimen but the relationship of this to the specimen structure is considered as a separate issue. 1 ref., 3 figs

Language-based multimedia information retrieval

NARCIS (Netherlands)

de Jong, Franciska M.G.; Gauvain, J.L.; Hiemstra, Djoerd; Netter, K.

2000-01-01

This paper describes various methods and approaches for language-based multimedia information retrieval, which have been developed in the projects POP-EYE and OLIVE and which will be developed further in the MUMIS project. All of these project aim at supporting automated indexing of video material

Three-dimensional ordered particulate structures: Method to retrieve characteristics from photonic band gap data

International Nuclear Information System (INIS)

Miskevich, Alexander A.; Loiko, Valery A.

2015-01-01

A method to retrieve characteristics of ordered particulate structures, such as photonic crystals, is proposed. It is based on the solution of the inverse problem using data on the photonic band gap (PBG). The quasicrystalline approximation (QCA) of the theory of multiple scattering of waves and the transfer matrix method (TMM) are used. Retrieval of the refractive index of particles is demonstrated. Refractive indices of the artificial opal particles are estimated using the published experimental data. - Highlights: • A method to retrieve characteristics of photonic crystals is proposed. • The method is based on the inverse problem solution using the photonic band gap data. • Retrieval of the refractive index of photonic crystal particles is demonstrated. • Retrieval results show inhomogeneous distribution of synthetic opal particle pores

A comparison of two methods for retrieving ICD-9-CM data: the effect of using an ontology-based method for handling terminology changes.

Science.gov (United States)

Yu, Alexander C; Cimino, James J

2011-04-01

Most existing controlled terminologies can be characterized as collections of terms, wherein the terms are arranged in a simple list or organized in a hierarchy. These kinds of terminologies are considered useful for standardizing terms and encoding data and are currently used in many existing information systems. However, they suffer from a number of limitations that make data reuse difficult. Relatively recently, it has been proposed that formal ontological methods can be applied to some of the problems of terminological design. Biomedical ontologies organize concepts (embodiments of knowledge about biomedical reality) whereas terminologies organize terms (what is used to code patient data at a certain point in time, based on the particular terminology version). However, the application of these methods to existing terminologies is not straightforward. The use of these terminologies is firmly entrenched in many systems, and what might seem to be a simple option of replacing these terminologies is not possible. Moreover, these terminologies evolve over time in order to suit the needs of users. Any methodology must therefore take these constraints into consideration, hence the need for formal methods of managing changes. Along these lines, we have developed a formal representation of the concept-term relation, around which we have also developed a methodology for management of terminology changes. The objective of this study was to determine whether our methodology would result in improved retrieval of data. Comparison of two methods for retrieving data encoded with terms from the International Classification of Diseases (ICD-9-CM), based on their recall when retrieving data for ICD-9-CM terms whose codes had changed but which had retained their original meaning (code change). Recall and interclass correlation coefficient. Statistically significant differences were detected (pontology-based ICD-9-CM data retrieval method that takes into account the effects of

Compounds in dictionary-based Cross-language information retrieval_revised

Directory of Open Access Journals (Sweden)

2002-01-01

Full Text Available Compound words form an important part of natural language. From the cross-lingual information retrieval (CLIR point of view it is important that many natural languages are highly productive with compounds, and translation resources cannot include entries for all compounds. Also, compounds are often content bearing words in a sentence. In Swedish, German and Finnish roughly one tenth of the words in a text prepared for information retrieval purposes are compounds. Important research questions concerning compound handling in dictionary-based cross-language information retrieval are 1 compound splitting into components, 2 normalisation of components, 3 translation of components and 4 query structuring for compounds and their components in the target language. The impact of compound processing on the performance of the cross-language information retrieval process is evaluated in this study and the results indicate that the effect is clearly positive.

A humming retrieval system based on music fingerprint

Science.gov (United States)

Han, Xingkai; Cao, Baiyu

2011-10-01

In this paper, we proposed an improved music information retrieval method utilizing the music fingerprint. The goal of this method is to represent the music with compressed musical information. Based on the selected MIDI files, which are generated automatically as our music target database, we evaluate the accuracy, effectiveness, and efficiency of this method. In this research we not only extract the feature sequence, which can represent the file effectively, from the query and melody database, but also make it possible for retrieving the results in an innovative way. We investigate on the influence of noise to the performance of our system. As experimental result shows, the retrieval accuracy arriving at up to91% without noise is pretty well

Generalized phase retrieval algorithm based on information measures

OpenAIRE

Shioya, Hiroyuki; Gohara, Kazutoshi

2006-01-01

An iterative phase retrieval algorithm based on the maximum entropy method (MEM) is presented. Introducing a new generalized information measure, we derive a novel class of algorithms which includes the conventionally used error reduction algorithm and a MEM-type iterative algorithm which is presented for the first time. These different phase retrieval methods are unified on the basis of the framework of information measures used in information theory.

A cloud-based framework for large-scale traditional Chinese medical record retrieval.

Science.gov (United States)

Liu, Lijun; Liu, Li; Fu, Xiaodong; Huang, Qingsong; Zhang, Xianwen; Zhang, Yin

2018-01-01

Electronic medical records are increasingly common in medical practice. The secondary use of medical records has become increasingly important. It relies on the ability to retrieve the complete information about desired patient populations. How to effectively and accurately retrieve relevant medical records from large- scale medical big data is becoming a big challenge. Therefore, we propose an efficient and robust framework based on cloud for large-scale Traditional Chinese Medical Records (TCMRs) retrieval. We propose a parallel index building method and build a distributed search cluster, the former is used to improve the performance of index building, and the latter is used to provide high concurrent online TCMRs retrieval. Then, a real-time multi-indexing model is proposed to ensure the latest relevant TCMRs are indexed and retrieved in real-time, and a semantics-based query expansion method and a multi- factor ranking model are proposed to improve retrieval quality. Third, we implement a template-based visualization method for displaying medical reports. The proposed parallel indexing method and distributed search cluster can improve the performance of index building and provide high concurrent online TCMRs retrieval. The multi-indexing model can ensure the latest relevant TCMRs are indexed and retrieved in real-time. The semantics expansion method and the multi-factor ranking model can enhance retrieval quality. The template-based visualization method can enhance the availability and universality, where the medical reports are displayed via friendly web interface. In conclusion, compared with the current medical record retrieval systems, our system provides some advantages that are useful in improving the secondary use of large-scale traditional Chinese medical records in cloud environment. The proposed system is more easily integrated with existing clinical systems and be used in various scenarios. Copyright © 2017. Published by Elsevier Inc.

A new method for the automatic retrieval of medical cases based on the RadLex ontology.

Science.gov (United States)

Spanier, A B; Cohen, D; Joskowicz, L

2017-03-01

The goal of medical case-based image retrieval (M-CBIR) is to assist radiologists in the clinical decision-making process by finding medical cases in large archives that most resemble a given case. Cases are described by radiology reports comprised of radiological images and textual information on the anatomy and pathology findings. The textual information, when available in standardized terminology, e.g., the RadLex ontology, and used in conjunction with the radiological images, provides a substantial advantage for M-CBIR systems. We present a new method for incorporating textual radiological findings from medical case reports in M-CBIR. The input is a database of medical cases, a query case, and the number of desired relevant cases. The output is an ordered list of the most relevant cases in the database. The method is based on a new case formulation, the Augmented RadLex Graph and an Anatomy-Pathology List. It uses a new case relatedness metric [Formula: see text] that prioritizes more specific medical terms in the RadLex tree over less specific ones and that incorporates the length of the query case. An experimental study on 8 CT queries from the 2015 VISCERAL 3D Case Retrieval Challenge database consisting of 1497 volumetric CT scans shows that our method has accuracy rates of 82 and 70% on the first 10 and 30 most relevant cases, respectively, thereby outperforming six other methods. The increasing amount of medical imaging data acquired in clinical practice constitutes a vast database of untapped diagnostically relevant information. This paper presents a new hybrid approach to retrieving the most relevant medical cases based on textual and image information.

Information content and sensitivity of the 3β + 2α lidar measurement system for aerosol microphysical retrievals

Science.gov (United States)

Burton, Sharon P.; Chemyakin, Eduard; Liu, Xu; Knobelspiesse, Kirk; Stamnes, Snorre; Sawamura, Patricia; Moore, Richard H.; Hostetler, Chris A.; Ferrare, Richard A.

2016-11-01

There is considerable interest in retrieving profiles of aerosol effective radius, total number concentration, and complex refractive index from lidar measurements of extinction and backscatter at several wavelengths. The combination of three backscatter channels plus two extinction channels (3β + 2α) is particularly important since it is believed to be the minimum configuration necessary for the retrieval of aerosol microphysical properties and because the technological readiness of lidar systems permits this configuration on both an airborne and future spaceborne instrument. The second-generation NASA Langley airborne High Spectral Resolution Lidar (HSRL-2) has been making 3β + 2α measurements since 2012. The planned NASA Aerosol/Clouds/Ecosystems (ACE) satellite mission also recommends the 3β + 2α combination.Here we develop a deeper understanding of the information content and sensitivities of the 3β + 2α system in terms of aerosol microphysical parameters of interest. We use a retrieval-free methodology to determine the basic sensitivities of the measurements independent of retrieval assumptions and constraints. We calculate information content and uncertainty metrics using tools borrowed from the optimal estimation methodology based on Bayes' theorem, using a simplified forward model look-up table, with no explicit inversion. The forward model is simplified to represent spherical particles, monomodal log-normal size distributions, and wavelength-independent refractive indices. Since we only use the forward model with no retrieval, the given simplified aerosol scenario is applicable as a best case for all existing retrievals in the absence of additional constraints. Retrieval-dependent errors due to mismatch between retrieval assumptions and true atmospheric aerosols are not included in this sensitivity study, and neither are retrieval errors that may be introduced in the inversion process. The choice of a simplified model adds clarity to the

Mutual information based feature selection for medical image retrieval

Science.gov (United States)

Zhi, Lijia; Zhang, Shaomin; Li, Yan

2018-04-01

In this paper, authors propose a mutual information based method for lung CT image retrieval. This method is designed to adapt to different datasets and different retrieval task. For practical applying consideration, this method avoids using a large amount of training data. Instead, with a well-designed training process and robust fundamental features and measurements, the method in this paper can get promising performance and maintain economic training computation. Experimental results show that the method has potential practical values for clinical routine application.

Image Retrieval Berdasarkan Fitur Warna, Bentuk, dan Tekstur

Directory of Open Access Journals (Sweden)

Rita Layona

2014-12-01

Full Text Available Along with the times, information retrieval is no longer just on textual data, but also the visual data. The technique was originally used is Text-Based Image Retrieval (TBIR, but the technique still has some shortcomings such as the relevance of the picture successfully retrieved, and the specific space required to store meta-data in the image. Seeing the shortage of Text-Based Image Retrieval techniques, then other techniques were developed, namely Image Retrieval based on content or commonly called Content Based Image Retrieval (CBIR. In this research, CBIR will be discussed based on color, shape and texture using a color histogram, Gabor and SIFT. This study aimed to compare the results of image retrieval with some of these techniques. The results obtained are by combining color, shape and texture features, the performance of the system can be improved.

MPEG-7-based description infrastructure for an audiovisual content analysis and retrieval system

Science.gov (United States)

Bailer, Werner; Schallauer, Peter; Hausenblas, Michael; Thallinger, Georg

2005-01-01

We present a case study of establishing a description infrastructure for an audiovisual content-analysis and retrieval system. The description infrastructure consists of an internal metadata model and access tool for using it. Based on an analysis of requirements, we have selected, out of a set of candidates, MPEG-7 as the basis of our metadata model. The openness and generality of MPEG-7 allow using it in broad range of applications, but increase complexity and hinder interoperability. Profiling has been proposed as a solution, with the focus on selecting and constraining description tools. Semantic constraints are currently only described in textual form. Conformance in terms of semantics can thus not be evaluated automatically and mappings between different profiles can only be defined manually. As a solution, we propose an approach to formalize the semantic constraints of an MPEG-7 profile using a formal vocabulary expressed in OWL, which allows automated processing of semantic constraints. We have defined the Detailed Audiovisual Profile as the profile to be used in our metadata model and we show how some of the semantic constraints of this profile can be formulated using ontologies. To work practically with the metadata model, we have implemented a MPEG-7 library and a client/server document access infrastructure.

Today's and tomorrow's retrieval practice in the audiovisual archive

NARCIS (Netherlands)

Huurnink, B.; Snoek, C.G.M.; de Rijke, M.; Smeulders, A.W.M.

2010-01-01

Content-based video retrieval is maturing to the point where it can be used in real-world retrieval practices. One such practice is the audiovisual archive, whose users increasingly require fine-grained access to broadcast television content. We investigate to what extent content-based video

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

Science.gov (United States)

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Information Retrieval Methods in Libraries and Information Centers ...

African Journals Online (AJOL)

The volumes of information created, generated and stored are immense that without adequate knowledge of information retrieval methods, the retrieval process for an information user would be cumbersome and frustrating. Studies have further revealed that information retrieval methods are essential in information centers ...

The Continuous Monitoring of Desert Dust using an Infrared-based Dust Detection and Retrieval Method

Science.gov (United States)

Duda, David P.; Minnis, Patrick; Trepte, Qing; Sun-Mack, Sunny

2006-01-01

Airborne dust and sand are significant aerosol sources that can impact the atmospheric and surface radiation budgets. Because airborne dust affects visibility and air quality, it is desirable to monitor the location and concentrations of this aerosol for transportation and public health. Although aerosol retrievals have been derived for many years using visible and near-infrared reflectance measurements from satellites, the detection and quantification of dust from these channels is problematic over bright surfaces, or when dust concentrations are large. In addition, aerosol retrievals from polar orbiting satellites lack the ability to monitor the progression and sources of dust storms. As a complement to current aerosol dust retrieval algorithms, multi-spectral thermal infrared (8-12 micron) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Meteosat-8 Spinning Enhanced Visible and Infrared Imager (SEVIRI) are used in the development of a prototype dust detection method and dust property retrieval that can monitor the progress of Saharan dust fields continuously, both night and day. The dust detection method is incorporated into the processing of CERES (Clouds and the Earth s Radiant Energy System) aerosol retrievals to produce dust property retrievals. Both MODIS (from Terra and Aqua) and SEVERI data are used to develop the method.

Comparison of two split-window methods for retrieving land surface temperature from MODIS data

Science.gov (United States)

Zhao, Shaohua; Qin, Qiming; Yang, Yonghui; Xiong, Yujiu; Qiu, Guoyu

2009-08-01

Land surface temperature (LST) is a key parameter in environment and earth science study, especially for monitoring drought. The objective of this work is a comparison of two split-window methods: Mao method and Sobrino method, for retrieving LST using MODIS (Moderate-resolution Imaging Spectroradiometer) data in North China Plain. The results show that the max, min and mean errors of Mao method are 1.33K, 1.54K and 0.13K lower than the standard LST product respectively; while those of Sobrino method are 0.73K, 1.46K and 1.50K higher than the standard respectively. Validation of the two methods using LST product based on weather stations shows a good agreement between the standard and Sobrino method, with RMSE of 1.17K, whereas RMSE of Mao method is 1.85K. Finally, the study introduces the Sobmao method, which is based on Sobrino method but simplifies the estimation of atmospheric water vapour content using Mao method. The Sobmao method has almost the same accuracy with Sobrino method. With high accuracy and simplification of water vapour content estimation, the Sobmao method is recommendable in LST inversion for good application in Ningxia region, the northwest China, with mean error of 0.33K and the RMSE value of 0.91K.

A differential optical absorption spectroscopy method for retrieval from ground-based Fourier transform spectrometers measurements of the direct solar beam

Science.gov (United States)

Huo, Yanfeng; Duan, Minzheng; Tian, Wenshou; Min, Qilong

2015-08-01

A differential optical absorption spectroscopy (DOAS)-like algorithm is developed to retrieve the column-averaged dryair mole fraction of carbon dioxide from ground-based hyper-spectral measurements of the direct solar beam. Different to the spectral fitting method, which minimizes the difference between the observed and simulated spectra, the ratios of multiple channel-pairs—one weak and one strong absorption channel—are used to retrieve from measurements of the shortwave infrared (SWIR) band. Based on sensitivity tests, a super channel-pair is carefully selected to reduce the effects of solar lines, water vapor, air temperature, pressure, instrument noise, and frequency shift on retrieval errors. The new algorithm reduces computational cost and the retrievals are less sensitive to temperature and H2O uncertainty than the spectral fitting method. Multi-day Total Carbon Column Observing Network (TCCON) measurements under clear-sky conditions at two sites (Tsukuba and Bremen) are used to derive xxxx for the algorithm evaluation and validation. The DOAS-like results agree very well with those of the TCCON algorithm after correction of an airmass-dependent bias.

Pulse retrieval algorithm for interferometric frequency-resolved optical gating based on differential evolution.

Science.gov (United States)

Hyyti, Janne; Escoto, Esmerando; Steinmeyer, Günter

2017-10-01

A novel algorithm for the ultrashort laser pulse characterization method of interferometric frequency-resolved optical gating (iFROG) is presented. Based on a genetic method, namely, differential evolution, the algorithm can exploit all available information of an iFROG measurement to retrieve the complex electric field of a pulse. The retrieval is subjected to a series of numerical tests to prove the robustness of the algorithm against experimental artifacts and noise. These tests show that the integrated error-correction mechanisms of the iFROG method can be successfully used to remove the effect from timing errors and spectrally varying efficiency in the detection. Moreover, the accuracy and noise resilience of the new algorithm are shown to outperform retrieval based on the generalized projections algorithm, which is widely used as the standard method in FROG retrieval. The differential evolution algorithm is further validated with experimental data, measured with unamplified three-cycle pulses from a mode-locked Ti:sapphire laser. Additionally introducing group delay dispersion in the beam path, the retrieval results show excellent agreement with independent measurements with a commercial pulse measurement device based on spectral phase interferometry for direct electric-field retrieval. Further experimental tests with strongly attenuated pulses indicate resilience of differential-evolution-based retrieval against massive measurement noise.

A 3D model retrieval approach based on Bayesian networks lightfield descriptor

Science.gov (United States)

Xiao, Qinhan; Li, Yanjun

2009-12-01

A new 3D model retrieval methodology is proposed by exploiting a novel Bayesian networks lightfield descriptor (BNLD). There are two key novelties in our approach: (1) a BN-based method for building lightfield descriptor; and (2) a 3D model retrieval scheme based on the proposed BNLD. To overcome the disadvantages of the existing 3D model retrieval methods, we explore BN for building a new lightfield descriptor. Firstly, 3D model is put into lightfield, about 300 binary-views can be obtained along a sphere, then Fourier descriptors and Zernike moments descriptors can be calculated out from binaryviews. Then shape feature sequence would be learned into a BN model based on BN learning algorithm; Secondly, we propose a new 3D model retrieval method by calculating Kullback-Leibler Divergence (KLD) between BNLDs. Beneficial from the statistical learning, our BNLD is noise robustness as compared to the existing methods. The comparison between our method and the lightfield descriptor-based approach is conducted to demonstrate the effectiveness of our proposed methodology.

Propagation based phase retrieval of simulated intensity measurements using artificial neural networks

Science.gov (United States)

Kemp, Z. D. C.

2018-04-01

Determining the phase of a wave from intensity measurements has many applications in fields such as electron microscopy, visible light optics, and medical imaging. Propagation based phase retrieval, where the phase is obtained from defocused images, has shown significant promise. There are, however, limitations in the accuracy of the retrieved phase arising from such methods. Sources of error include shot noise, image misalignment, and diffraction artifacts. We explore the use of artificial neural networks (ANNs) to improve the accuracy of propagation based phase retrieval algorithms applied to simulated intensity measurements. We employ a phase retrieval algorithm based on the transport-of-intensity equation to obtain the phase from simulated micrographs of procedurally generated specimens. We then train an ANN with pairs of retrieved and exact phases, and use the trained ANN to process a test set of retrieved phase maps. The total error in the phase is significantly reduced using this method. We also discuss a variety of potential extensions to this work.

Rocchio-based relevance feedback in video event retrieval

NARCIS (Netherlands)

Pingen, G.L.J.; de Boer, M.H.T.; Aly, Robin; Amsaleg, Laurent; Guðmundsson, Gylfi Þór; Gurrin, Cathal; Jónsson, Björn Þór; Satoh, Shin’ichi

This paper investigates methods for user and pseudo relevance feedback in video event retrieval. Existing feedback methods achieve strong performance but adjust the ranking based on few individual examples. We propose a relevance feedback algorithm (ARF) derived from the Rocchio method, which is a

Robust keyword retrieval method for OCRed text

Science.gov (United States)

Fujii, Yusaku; Takebe, Hiroaki; Tanaka, Hiroshi; Hotta, Yoshinobu

2011-01-01

Document management systems have become important because of the growing popularity of electronic filing of documents and scanning of books, magazines, manuals, etc., through a scanner or a digital camera, for storage or reading on a PC or an electronic book. Text information acquired by optical character recognition (OCR) is usually added to the electronic documents for document retrieval. Since texts generated by OCR generally include character recognition errors, robust retrieval methods have been introduced to overcome this problem. In this paper, we propose a retrieval method that is robust against both character segmentation and recognition errors. In the proposed method, the insertion of noise characters and dropping of characters in the keyword retrieval enables robustness against character segmentation errors, and character substitution in the keyword of the recognition candidate for each character in OCR or any other character enables robustness against character recognition errors. The recall rate of the proposed method was 15% higher than that of the conventional method. However, the precision rate was 64% lower.

Music Retrieval Based on the Relation between Color Association and Lyrics

Science.gov (United States)

Nakamur, Tetsuaki; Utsumi, Akira; Sakamoto, Maki

Various methods for music retrieval have been proposed. Recently, many researchers are tackling developing methods based on the relationship between music and feelings. In our previous psychological study, we found that there was a significant correlation between colors evoked from songs and colors evoked only from lyrics, and showed that the music retrieval system using lyrics could be developed. In this paper, we focus on the relationship among music, lyrics and colors, and propose a music retrieval method using colors as queries and analyzing lyrics. This method estimates colors evoked from songs by analyzing lyrics of the songs. On the first step of our method, words associated with colors are extracted from lyrics. We assumed two types of methods to extract words associated with colors. In the one of two methods, the words are extracted based on the result of a psychological experiment. In the other method, in addition to the words extracted based on the result of the psychological experiment, the words from corpora for the Latent Semantic Analysis are extracted. On the second step, colors evoked from the extracted words are compounded, and the compounded colors are regarded as those evoked from the song. On the last step, colors as queries are compared with colors estimated from lyrics, and the list of songs is presented based on similarities. We evaluated the two methods described above and found that the method based on the psychological experiment and corpora performed better than the method only based on the psychological experiment. As a result, we showed that the method using colors as queries and analyzing lyrics is effective for music retrieval.

108 Information Retrieval Methods in Libraries and Information ...

African Journals Online (AJOL)

User

without adequate knowledge of information retrieval methods, the retrieval process for an ... discusses the concept of Information retrieval, the various information ..... Other advantages of automatic indexing are the maintenance of consistency.

Hardware emulation of Memristor based Ternary Content Addressable Memory

KAUST Repository

Bahloul, Mohamed A.

2017-12-13

MTCAM (Memristor Ternary Content Addressable Memory) is a special purpose storage medium in which data could be retrieved based on the stored content. Using Memristors as the main storage element provides the potential of achieving higher density and more efficient solutions than conventional methods. A key missing item in the validation of such approaches is the wide spread availability of hardware emulation platforms that can provide reliable and repeatable performance statistics. In this paper, we present a hardware MTCAM emulation based on 2-Transistors-2Memristors (2T2M) bit-cell. It builds on a bipolar memristor model with storing and fetching capabilities based on the actual current-voltage behaviour. The proposed design offers a flexible verification environment with quick design revisions, high execution speeds and powerful debugging techniques. The proposed design is modeled using VHDL and prototyped on Xilinx Virtex® FPGA.

Hardware emulation of Memristor based Ternary Content Addressable Memory

KAUST Repository

Bahloul, Mohamed A.; Naous, Rawan; Masmoudi, M.

2017-01-01

MTCAM (Memristor Ternary Content Addressable Memory) is a special purpose storage medium in which data could be retrieved based on the stored content. Using Memristors as the main storage element provides the potential of achieving higher density and more efficient solutions than conventional methods. A key missing item in the validation of such approaches is the wide spread availability of hardware emulation platforms that can provide reliable and repeatable performance statistics. In this paper, we present a hardware MTCAM emulation based on 2-Transistors-2Memristors (2T2M) bit-cell. It builds on a bipolar memristor model with storing and fetching capabilities based on the actual current-voltage behaviour. The proposed design offers a flexible verification environment with quick design revisions, high execution speeds and powerful debugging techniques. The proposed design is modeled using VHDL and prototyped on Xilinx Virtex® FPGA.

Aerosol Retrievals from Proposed Satellite Bistatic Lidar Observations: Algorithm and Information Content

Science.gov (United States)

Alexandrov, M. D.; Mishchenko, M. I.

2017-12-01

Accurate aerosol retrievals from space remain quite challenging and typically involve solving a severely ill-posed inverse scattering problem. We suggested to address this ill-posedness by flying a bistatic lidar system. Such a system would consist of formation flying constellation of a primary satellite equipped with a conventional monostatic (backscattering) lidar and an additional platform hosting a receiver of the scattered laser light. If successfully implemented, this concept would combine the measurement capabilities of a passive multi-angle multi-spectral polarimeter with the vertical profiling capability of a lidar. Thus, bistatic lidar observations will be free of deficiencies affecting both monostatic lidar measurements (caused by the highly limited information content) and passive photopolarimetric measurements (caused by vertical integration and surface reflection).We present a preliminary aerosol retrieval algorithm for a bistatic lidar system consisting of a high spectral resolution lidar (HSRL) and an additional receiver flown in formation with it at a scattering angle of 165 degrees. This algorithm was applied to synthetic data generated using Mie-theory computations. The model/retrieval parameters in our tests were the effective radius and variance of the aerosol size distribution, complex refractive index of the particles, and their number concentration. Both mono- and bimodal aerosol mixtures were considered. Our algorithm allowed for definitive evaluation of error propagation from measurements to retrievals using a Monte Carlo technique, which involves random distortion of the observations and statistical characterization of the resulting retrieval errors. Our tests demonstrated that supplementing a conventional monostatic HSRL with an additional receiver dramatically increases the information content of the measurements and allows for a sufficiently accurate characterization of tropospheric aerosols.

Natural texture retrieval based on perceptual similarity measurement

Science.gov (United States)

Gao, Ying; Dong, Junyu; Lou, Jianwen; Qi, Lin; Liu, Jun

2018-04-01

A typical texture retrieval system performs feature comparison and might not be able to make human-like judgments of image similarity. Meanwhile, it is commonly known that perceptual texture similarity is difficult to be described by traditional image features. In this paper, we propose a new texture retrieval scheme based on texture perceptual similarity. The key of the proposed scheme is that prediction of perceptual similarity is performed by learning a non-linear mapping from image features space to perceptual texture space by using Random Forest. We test the method on natural texture dataset and apply it on a new wallpapers dataset. Experimental results demonstrate that the proposed texture retrieval scheme with perceptual similarity improves the retrieval performance over traditional image features.

Advances in audio source seperation and multisource audio content retrieval

Science.gov (United States)

Vincent, Emmanuel

2012-06-01

Audio source separation aims to extract the signals of individual sound sources from a given recording. In this paper, we review three recent advances which improve the robustness of source separation in real-world challenging scenarios and enable its use for multisource content retrieval tasks, such as automatic speech recognition (ASR) or acoustic event detection (AED) in noisy environments. We present a Flexible Audio Source Separation Toolkit (FASST) and discuss its advantages compared to earlier approaches such as independent component analysis (ICA) and sparse component analysis (SCA). We explain how cues as diverse as harmonicity, spectral envelope, temporal fine structure or spatial location can be jointly exploited by this toolkit. We subsequently present the uncertainty decoding (UD) framework for the integration of audio source separation and audio content retrieval. We show how the uncertainty about the separated source signals can be accurately estimated and propagated to the features. Finally, we explain how this uncertainty can be efficiently exploited by a classifier, both at the training and the decoding stage. We illustrate the resulting performance improvements in terms of speech separation quality and speaker recognition accuracy.

Image Information Retrieval: An Overview of Current Research

OpenAIRE

Abby A. Goodrum

2000-01-01

This paper provides an overview of current research in image information retrieval and provides an outline of areas for future research. The approach is broad and interdisciplinary and focuses on three aspects of image research (IR): text-based retrieval, content-based retrieval, and user interactions with image information retrieval systems. The review concludes with a call for image retrieval evaluation studies similar to TREC.

Information Content of Aerosol Retrievals in the Sunglint Region

Science.gov (United States)

Ottaviani, M.; Knobelspiesse, K.; Cairns, B.; Mishchenko, M.

2013-01-01

We exploit quantitative metrics to investigate the information content in retrievals of atmospheric aerosol parameters (with a focus on single-scattering albedo), contained in multi-angle and multi-spectral measurements with sufficient dynamical range in the sunglint region. The simulations are performed for two classes of maritime aerosols with optical and microphysical properties compiled from measurements of the Aerosol Robotic Network. The information content is assessed using the inverse formalism and is compared to that deriving from observations not affected by sunglint. We find that there indeed is additional information in measurements containing sunglint, not just for single-scattering albedo, but also for aerosol optical thickness and the complex refractive index of the fine aerosol size mode, although the amount of additional information varies with aerosol type.

Developing an A Priori Database for Passive Microwave Snow Water Retrievals Over Ocean

Science.gov (United States)

Yin, Mengtao; Liu, Guosheng

2017-12-01

A physically optimized a priori database is developed for Global Precipitation Measurement Microwave Imager (GMI) snow water retrievals over ocean. The initial snow water content profiles are derived from CloudSat Cloud Profiling Radar (CPR) measurements. A radiative transfer model in which the single-scattering properties of nonspherical snowflakes are based on the discrete dipole approximate results is employed to simulate brightness temperatures and their gradients. Snow water content profiles are then optimized through a one-dimensional variational (1D-Var) method. The standard deviations of the difference between observed and simulated brightness temperatures are in a similar magnitude to the observation errors defined for observation error covariance matrix after the 1D-Var optimization, indicating that this variational method is successful. This optimized database is applied in a Bayesian retrieval snow water algorithm. The retrieval results indicated that the 1D-Var approach has a positive impact on the GMI retrieved snow water content profiles by improving the physical consistency between snow water content profiles and observed brightness temperatures. Global distribution of snow water contents retrieved from the a priori database is compared with CloudSat CPR estimates. Results showed that the two estimates have a similar pattern of global distribution, and the difference of their global means is small. In addition, we investigate the impact of using physical parameters to subset the database on snow water retrievals. It is shown that using total precipitable water to subset the database with 1D-Var optimization is beneficial for snow water retrievals.

MR-based water content estimation in cartilage: design and validation of a method

DEFF Research Database (Denmark)

Shiguetomi Medina, Juan Manuel; Kristiansen, Maja Sophie; Ringgaard, Steffen

Purpose: Design and validation of an MR-based method that allows the calculation of the water content in cartilage tissue. Methods and Materials: Cartilage tissue T1 map based water content MR sequences were used on a 37 Celsius degree stable system. The T1 map intensity signal was analyzed on 6...... cartilage samples from living animals (pig) and on 8 gelatin samples which water content was already known. For the data analysis a T1 intensity signal map software analyzer used. Finally, the method was validated after measuring and comparing 3 more cartilage samples in a living animal (pig). The obtained...... map based water content sequences can provide information that, after being analyzed using a T1-map analysis software, can be interpreted as the water contained inside a cartilage tissue. The amount of water estimated using this method was similar to the one obtained at the dry-freeze procedure...

Ontology-based Information Retrieval

DEFF Research Database (Denmark)

Styltsvig, Henrik Bulskov

In this thesis, we will present methods for introducing ontologies in information retrieval. The main hypothesis is that the inclusion of conceptual knowledge such as ontologies in the information retrieval process can contribute to the solution of major problems currently found in information...... retrieval. This utilization of ontologies has a number of challenges. Our focus is on the use of similarity measures derived from the knowledge about relations between concepts in ontologies, the recognition of semantic information in texts and the mapping of this knowledge into the ontologies in use......, as well as how to fuse together the ideas of ontological similarity and ontological indexing into a realistic information retrieval scenario. To achieve the recognition of semantic knowledge in a text, shallow natural language processing is used during indexing that reveals knowledge to the level of noun...

Validation of GOES-Derived Surface Radiation Using NOAA's Physical Retrieval Method

Energy Technology Data Exchange (ETDEWEB)

Habte, A.; Sengupta, M.; Wilcox, S.

2013-01-01

This report was part of a multiyear collaboration with the University of Wisconsin and the National Oceanic and Atmospheric Administration (NOAA) to produce high-quality, satellite-based, solar resource datasets for the United States. High-quality, solar resource assessment accelerates technology deployment by making a positive impact on decision making and reducing uncertainty in investment decisions. Satellite-based solar resource datasets are used as a primary source in solar resource assessment. This is mainly because satellites provide larger areal coverage and longer periods of record than ground-based measurements. With the advent of newer satellites with increased information content and faster computers that can process increasingly higher data volumes, methods that were considered too computationally intensive are now feasible. One class of sophisticated methods for retrieving solar resource information from satellites is a two-step, physics-based method that computes cloud properties and uses the information in a radiative transfer model to compute solar radiation. This method has the advantage of adding additional information as satellites with newer channels come on board. This report evaluates the two-step method developed at NOAA and adapted for solar resource assessment for renewable energy with the goal of identifying areas that can be improved in the future.

Conjugate gradient method for phase retrieval based on the Wirtinger derivative.

Science.gov (United States)

Wei, Zhun; Chen, Wen; Qiu, Cheng-Wei; Chen, Xudong

2017-05-01

A conjugate gradient Wirtinger flow (CG-WF) algorithm for phase retrieval is proposed in this paper. It is shown that, compared with recently reported Wirtinger flow and its modified methods, the proposed CG-WF algorithm is able to dramatically accelerate the convergence rate while keeping the dominant computational cost of each iteration unchanged. We numerically illustrate the effectiveness of our method in recovering 1D Gaussian signals and 2D natural color images under both Gaussian and coded diffraction pattern models.

Collaborative Video Search Combining Video Retrieval with Human-Based Visual Inspection

NARCIS (Netherlands)

Hudelist, M.A.; Cobârzan, C.; Beecks, C.; van de Werken, Rob; Kletz, S.; Hürst, W.O.; Schoeffmann, K.

2016-01-01

We propose a novel video browsing approach that aims at optimally integrating traditional, machine-based retrieval methods with an interface design optimized for human browsing performance. Advanced video retrieval and filtering (e.g., via color and motion signatures, and visual concepts) on a

Information content of ozone retrieval algorithms

Science.gov (United States)

Rodgers, C.; Bhartia, P. K.; Chu, W. P.; Curran, R.; Deluisi, J.; Gille, J. C.; Hudson, R.; Mateer, C.; Rusch, D.; Thomas, R. J.

1989-01-01

The algorithms are characterized that were used for production processing by the major suppliers of ozone data to show quantitatively: how the retrieved profile is related to the actual profile (This characterizes the altitude range and vertical resolution of the data); the nature of systematic errors in the retrieved profiles, including their vertical structure and relation to uncertain instrumental parameters; how trends in the real ozone are reflected in trends in the retrieved ozone profile; and how trends in other quantities (both instrumental and atmospheric) might appear as trends in the ozone profile. No serious deficiencies were found in the algorithms used in generating the major available ozone data sets. As the measurements are all indirect in someway, and the retrieved profiles have different characteristics, data from different instruments are not directly comparable.

Development of methods for determination of PAH based on measured CO-content

International Nuclear Information System (INIS)

Ingman, Rolf; Schuster, Robert

2001-02-01

The aim of the project 'Development of methods for determination of PAH based on measured CO-content' is to investigate the possibility to develop a method for continuous optimisation of NO x -emissions by decreased air ratio, without significant increase of polyaromatic hydrocarbons such as PAH. The general idea has been to find a indirect online method to predict the emissions of heavier hydrocarbons by: - creating a correlation between the content of CO and PAH, - controlling the air ratio by the CO-content, and - integrating the calculated PAH-content from CO-content. Today many boilers are operated with a low air ratio to minimise the NO x content and the NO x -fee. A low ratio increases the risk of high CO contents in the flue gas as well as increased contents of VOC and PAH. Other boilers are operated with high air ratios in order to minimise the CO content, which in some cases will result in unnecessary high NO x emissions. One of the main difficulties in optimising the air ratio to the most environmental friendly level is the lack of a suitable and well proven PAH instrument. There are today no available instruments for instantaneous and continuous measurement of PAH. PAH is normally measured as an average value during a period of at least one hour. It is not possible to detect short peaks. The development of the CO-method has been based on data from a CFB-boiler in Korsta in Sundsvall (Vaermeforskrapport 541). The data shows a clear correlation between THC and CO. The correlation seems to be mostly dependent of moisture content and load. The development presented in the report shows that it is possible to find a method to predict the PAH content from the CO-content in the flue gas. The next phase aims to improve and implement the method, by measurements and adaptation in a plant. The practical use of the method is as a tool to optimise the emission of CO, NO x , THC and PAH and/or to predict the PAH-emission during continuous operation

Efficient view based 3-D object retrieval using Hidden Markov Model

Science.gov (United States)

Jain, Yogendra Kumar; Singh, Roshan Kumar

2013-12-01

Recent research effort has been dedicated to view based 3-D object retrieval, because of highly discriminative property of 3-D object and has multi view representation. The state-of-art method is highly depending on their own camera array setting for capturing views of 3-D object and use complex Zernike descriptor, HAC for representative view selection which limit their practical application and make it inefficient for retrieval. Therefore, an efficient and effective algorithm is required for 3-D Object Retrieval. In order to move toward a general framework for efficient 3-D object retrieval which is independent of camera array setting and avoidance of representative view selection, we propose an Efficient View Based 3-D Object Retrieval (EVBOR) method using Hidden Markov Model (HMM). In this framework, each object is represented by independent set of view, which means views are captured from any direction without any camera array restriction. In this, views are clustered (including query view) to generate the view cluster, which is then used to build the query model with HMM. In our proposed method, HMM is used in twofold: in the training (i.e. HMM estimate) and in the retrieval (i.e. HMM decode). The query model is trained by using these view clusters. The EVBOR query model is worked on the basis of query model combining with HMM. The proposed approach remove statically camera array setting for view capturing and can be apply for any 3-D object database to retrieve 3-D object efficiently and effectively. Experimental results demonstrate that the proposed scheme has shown better performance than existing methods. [Figure not available: see fulltext.

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.

Application of Tikhonov regularization method to wind retrieval from scatterometer data II: cyclone wind retrieval with consideration of rain

International Nuclear Information System (INIS)

Zhong Jian; Huang Si-Xun; Fei Jian-Fang; Du Hua-Dong; Zhang Liang

2011-01-01

According to the conclusion of the simulation experiments in paper I, the Tikhonov regularization method is applied to cyclone wind retrieval with a rain-effect-considering geophysical model function (called GMF+Rain). The GMF+Rain model which is based on the NASA scatterometer-2 (NSCAT2) GMF is presented to compensate for the effects of rain on cyclone wind retrieval. With the multiple solution scheme (MSS), the noise of wind retrieval is effectively suppressed, but the influence of the background increases. It will cause a large wind direction error in ambiguity removal when the background error is large. However, this can be mitigated by the new ambiguity removal method of Tikhonov regularization as proved in the simulation experiments. A case study on an extratropical cyclone of hurricane observed with SeaWinds at 25-km resolution shows that the retrieved wind speed for areas with rain is in better agreement with that derived from the best track analysis for the GMF+Rain model, but the wind direction obtained with the two-dimensional variational (2DVAR) ambiguity removal is incorrect. The new method of Tikhonov regularization effectively improves the performance of wind direction ambiguity removal through choosing appropriate regularization parameters and the retrieved wind speed is almost the same as that obtained from the 2DVAR. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

Science.gov (United States)

Verrelst, Jochem; Malenovský, Zbyněk; Van der Tol, Christiaan; Camps-Valls, Gustau; Gastellu-Etchegorry, Jean-Philippe; Lewis, Philip; North, Peter; Moreno, Jose

2018-06-01

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.

Content-based Image Hiding Method for Secure Network Biometric Verification

Directory of Open Access Journals (Sweden)

Xiangjiu Che

2011-08-01

Full Text Available For secure biometric verification, most existing methods embed biometric information directly into the cover image, but content correlation analysis between the biometric image and the cover image is often ignored. In this paper, we propose a novel biometric image hiding approach based on the content correlation analysis to protect the network-based transmitted image. By using principal component analysis (PCA, the content correlation between the biometric image and the cover image is firstly analyzed. Then based on particle swarm optimization (PSO algorithm, some regions of the cover image are selected to represent the biometric image, in which the cover image can carry partial content of the biometric image. As a result of the correlation analysis, the unrepresented part of the biometric image is embedded into the cover image by using the discrete wavelet transform (DWT. Combined with human visual system (HVS model, this approach makes the hiding result perceptually invisible. The extensive experimental results demonstrate that the proposed hiding approach is robust against some common frequency and geometric attacks; it also provides an effective protection for the secure biometric verification.

Web-Scale Discovery Services Retrieve Relevant Results in Health Sciences Topics Including MEDLINE Content

Directory of Open Access Journals (Sweden)

Elizabeth Margaret Stovold

2017-06-01

Full Text Available A Review of: Hanneke, R., & O’Brien, K. K. (2016. Comparison of three web-scale discovery services for health sciences research. Journal of the Medical Library Association, 104(2, 109-117. http://dx.doi.org/10.3163/1536-5050.104.2.004 Abstract Objective – To compare the results of health sciences search queries in three web-scale discovery (WSD services for relevance, duplicate detection, and retrieval of MEDLINE content. Design – Comparative evaluation and bibliometric study. Setting – Six university libraries in the United States of America. Subjects – Three commercial WSD services: Primo, Summon, and EBSCO Discovery Service (EDS. Methods – The authors collected data at six universities, including their own. They tested each of the three WSDs at two data collection sites. However, since one of the sites was using a legacy version of Summon that was due to be upgraded, data collected for Summon at this site were considered obsolete and excluded from the analysis. The authors generated three questions for each of six major health disciplines, then designed simple keyword searches to mimic typical student search behaviours. They captured the first 20 results from each query run at each test site, to represent the first “page” of results, giving a total of 2,086 total search results. These were independently assessed for relevance to the topic. Authors resolved disagreements by discussion, and calculated a kappa inter-observer score. They retained duplicate records within the results so that the duplicate detection by the WSDs could be compared. They assessed MEDLINE coverage by the WSDs in several ways. Using precise strategies to generate a relevant set of articles, they conducted one search from each of the six disciplines in PubMed so that they could compare retrieval of MEDLINE content. These results were cross-checked against the first 20 results from the corresponding query in the WSDs. To aid investigation of overall

Understanding the aerosol information content in multi-spectral reflectance measurements using a synergetic retrieval algorithm

Directory of Open Access Journals (Sweden)

D. Martynenko

2010-11-01

Full Text Available An information content analysis for multi-wavelength SYNergetic AErosol Retrieval algorithm SYNAER was performed to quantify the number of independent pieces of information that can be retrieved. In particular, the capability of SYNAER to discern various aerosol types is assessed. This information content depends on the aerosol optical depth, the surface albedo spectrum and the observation geometry. The theoretical analysis is performed for a large number of scenarios with various geometries and surface albedo spectra for ocean, soil and vegetation. When the surface albedo spectrum and its accuracy is known under cloud-free conditions, reflectance measurements used in SYNAER is able to provide for 2–4° of freedom that can be attributed to retrieval parameters: aerosol optical depth, aerosol type and surface albedo.

The focus of this work is placed on an information content analysis with emphasis to the aerosol type classification. This analysis is applied to synthetic reflectance measurements for 40 predefined aerosol mixtures of different basic components, given by sea salt, mineral dust, biomass burning and diesel aerosols, water soluble and water insoluble aerosols. The range of aerosol parameters considered through the 40 mixtures covers the natural variability of tropospheric aerosols. After the information content analysis performed in Holzer-Popp et al. (2008 there was a necessity to compare derived degrees of freedom with retrieved aerosol optical depth for different aerosol types, which is the main focus of this paper.

The principle component analysis was used to determine the correspondence between degrees of freedom for signal in the retrieval and derived aerosol types. The main results of the analysis indicate correspondence between the major groups of the aerosol types, which are: water soluble aerosol, soot, mineral dust and sea salt and degrees of freedom in the algorithm and show the ability of the SYNAER to

Optical multiple-image encryption based on multiplane phase retrieval and interference

International Nuclear Information System (INIS)

Chen, Wen; Chen, Xudong

2011-01-01

In this paper, we propose a new method for optical multiple-image encryption based on multiplane phase retrieval and interference. An optical encoding system is developed in the Fresnel domain. A phase-only map is iteratively extracted based on a multiplane phase retrieval algorithm, and multiple plaintexts are simultaneously encrypted. Subsequently, the extracted phase-only map is further encrypted into two phase-only masks based on a non-iterative interference algorithm. During image decryption, the advantages and security of the proposed optical cryptosystem are analyzed. Numerical results are presented to demonstrate the validity of the proposed optical multiple-image encryption method

Scalable Integrated Region-Based Image Retrieval Using IRM and Statistical Clustering.

Science.gov (United States)

Wang, James Z.; Du, Yanping

Statistical clustering is critical in designing scalable image retrieval systems. This paper presents a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (Integrated Region Matching), a measure developed to evaluate overall similarity between images…

MR-based Water Content Estimation in Cartilage: Design and Validation of a Method

DEFF Research Database (Denmark)

Shiguetomi Medina, Juan Manuel; Kristiansen, Maja Sofie; Ringgaard, Steffen

2012-01-01

Objective Design and validation of an MR-based method that allows the calculation of the water content in cartilage tissue. Material and Methods We modified and adapted to cartilage tissue T1 map based water content MR sequences commonly used in the neurology field. Using a 37 Celsius degree stable...... was costumed and programmed. Finally, we validated the method after measuring and comparing 3 more cartilage samples in a living animal (pig). The obtained data was analyzed and the water content calculated. Then, the same samples were freeze-dried (this technique allows to take out all the water that a tissue...... contains) and we measured the water they contained. Results We could reproduce twice the 37 Celsius degree system and could perform the measurements in a similar way. We found that the MR T1 map based water content sequences can provide information that, after being analyzed with a special software, can...

An Abstraction-Based Data Model for Information Retrieval

Science.gov (United States)

McAllister, Richard A.; Angryk, Rafal A.

Language ontologies provide an avenue for automated lexical analysis that may be used to supplement existing information retrieval methods. This paper presents a method of information retrieval that takes advantage of WordNet, a lexical database, to generate paths of abstraction, and uses them as the basis for an inverted index structure to be used in the retrieval of documents from an indexed corpus. We present this method as a entree to a line of research on using ontologies to perform word-sense disambiguation and improve the precision of existing information retrieval techniques.

Feature hashing for fast image retrieval

Science.gov (United States)

Yan, Lingyu; Fu, Jiarun; Zhang, Hongxin; Yuan, Lu; Xu, Hui

2018-03-01

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

A new software suite for NO2 vertical profile retrieval from ground-based zenith-sky spectrometers

International Nuclear Information System (INIS)

Denis, L.; Roscoe, H.K.; Chipperfield, M.P.; Roozendael, M. van; Goutail, F.

2005-01-01

Here we present an operational method to improve accuracy and information content of ground-based measurements of stratospheric NO 2 . The motive is to improve the investigation of trends in NO 2 , and is important because the current trend in NO 2 appears to contradict the trend in its source, suggesting that the stratospheric circulation has changed. To do so, a new software package for retrieving NO 2 vertical profiles from slant columns measured by zenith-sky spectrometers has been created. It uses a Rodgers optimal linear inverse method coupled with a radiative transfer model for calculations of transfer functions between profiles and columns, and a chemical box model for taking into account the NO 2 variations during twilight and during the day. Each model has parameters that vary according to season and location. Forerunners of each model have been previously validated. The scheme maps random errors in the measurements and systematic errors in the models and their parameters on to the retrieved profiles. Initialisation for models is derived from well-established climatologies. The software has been tested by comparing retrieved profiles to simultaneous balloon-borne profiles at mid-latitudes in spring

Semantics-Based Intelligent Indexing and Retrieval of Digital Images - A Case Study

Science.gov (United States)

Osman, Taha; Thakker, Dhavalkumar; Schaefer, Gerald

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they typically rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this chapter we present a semantically enabled image annotation and retrieval engine that is designed to satisfy the requirements of commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as presenting our initial thoughts on exploiting lexical databases for explicit semantic-based query expansion.

Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos.

Science.gov (United States)

André, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2011-01-01

Evaluating content-based retrieval (CBR) is challenging because it requires an adequate ground-truth. When the available groundtruth is limited to textual metadata such as pathological classes, retrieval results can only be evaluated indirectly, for example in terms of classification performance. In this study we first present a tool to generate perceived similarity ground-truth that enables direct evaluation of endomicroscopic video retrieval. This tool uses a four-points Likert scale and collects subjective pairwise similarities perceived by multiple expert observers. We then evaluate against the generated ground-truth a previously developed dense bag-of-visual-words method for endomicroscopic video retrieval. Confirming the results of previous indirect evaluation based on classification, our direct evaluation shows that this method significantly outperforms several other state-of-the-art CBR methods. In a second step, we propose to improve the CBR method by learning an adjusted similarity metric from the perceived similarity ground-truth. By minimizing a margin-based cost function that differentiates similar and dissimilar video pairs, we learn a weight vector applied to the visual word signatures of videos. Using cross-validation, we demonstrate that the learned similarity distance is significantly better correlated with the perceived similarity than the original visual-word-based distance.

Phase retrieval with the reverse projection method in the presence of object's scattering

International Nuclear Information System (INIS)

Wang, Zhili; Gao, Kun; Wang, Dajiang

2017-01-01

X-ray grating interferometry can provide substantially increased contrast over traditional attenuation-based techniques in biomedical applications, and therefore novel and complementary information. Recently, special attention has been paid to quantitative phase retrieval in X-ray grating interferometry, which is mandatory to perform phase tomography, to achieve material identification, etc. An innovative approach, dubbed “Reverse Projection” (RP), has been developed for quantitative phase retrieval. The RP method abandons grating scanning completely, and is thus advantageous in terms of higher efficiency and reduced radiation damage. Therefore, it is expected that this novel method would find its potential in preclinical and clinical implementations. Strictly speaking, the reverse projection method is applicable for objects exhibiting only absorption and refraction. In this contribution, we discuss the phase retrieval with the reverse projection method for general objects with absorption, refraction and scattering simultaneously. Especially, we investigate the influence of the object's scattering on the retrieved refraction signal. Both theoretical analysis and numerical experiments are performed. The results show that the retrieved refraction signal is the product of object's refraction and scattering signals for small values. In the case of a strong scattering, the reverse projection method cannot provide reliable phase retrieval. Those presented results will guide the use of the reverse projection method for future practical applications, and help to explain some possible artifacts in the retrieved images and/or reconstructed slices. - Highlights: • Accurate phase retrieval by the reverse projection method without object's scattering. • Retrieved refraction signal contaminated by the object's scattering. • Refraction signal underestimated by the reverse projection method. • Guide the use of the reverse projection method for

Comparison of methods for measurement and retrieval of SIF with tower based sensors

Science.gov (United States)

Kornfeld, A.; Berry, J. A.

2017-12-01

As the popularity of solar induced fluorescence (SIF) measurement increases, the number of ways to measure and process the data has also increased, leaving a bewildering array of choices for the practitioner. To help clarify the advantages and disadvantages of several methods, we modified our foreoptic, Rotaprism, to measure spectra using either bi-hemispheric (cosine correcting diffusers on both upward and downward views) or hemispherical-conical views (only the upward view is cosine corrected). To test spatial sensitivity of each optic, we recorded data after moving the device relatively short distances - 1-2x the sensor's height above the canopy. When using conical measurements, measured SIF varied by as much as 100% across locations, whereas bi-hemispherical measurements were nearly unaffected by the moves. Reflectance indexes such as NDVI, PRI, NIRv were also spatially sensitive for the conical measurements. We also compared retrievals using either the O2A band or the adjacent Fraunhofer band to examine the relative advantages of each retrieval band for full-day retrievals. Finally, we investigated how choice of retrieval algorithm (SVD, FLD, SFM) affects the computed results. The primary site for this experiment was a California bunchgrass/tallgrass field. Additional data from the Brazilian Amazon will also be used, where appropriate, to support our conclusions.

A Novel Technique for Assessing Antioxidant Concentration in Retrieved UHMWPE.

Science.gov (United States)

Currier, Barbara H; Van Citters, Douglas W

2017-05-01

identification of antioxidant content. Paired Student's t-tests were used to compare as-retrieved articular antioxidant index with expected antioxidant index (the bulk value for blended antioxidants where constant antioxidant content is expected throughout and the extrapolated original vitamin E concentration at the articular surface based on the as-manufactured vitamin E concentration gradient). Linear regression was used for each of the retrievals to evaluate the correlation of antioxidant index to ester content with the goal of extrapolation to the antioxidant index at zero ester content. On average, vitamin E index at the articular surface (0.04 ± 0.03) was reduced compared with expected vitamin E index (0.09 ± 0.04; 95% confidence interval [CI] of the difference, 0.04-0.07; p antioxidant indices at zero absorbed ester index. Absorbed esters from time in vivo caused erroneous values of antioxidant index to be calculated. However, hexane extraction to remove absorbed species also removed diffused vitamin E. Correlating antioxidant indices with ester content, measured by FTIR in unextracted antioxidant retrievals, provides a nonaltered method for estimating actual articular surface vitamin E index and demonstrates that there was no measurable elution in these short-term retrievals. Assessing antioxidant content in retrieved polyethylene inserts is important to determine how much of the antioxidant remains in place to prevent oxidation of the polyethylene over time in vivo. Retrieval analyses reporting antioxidant content must account for absorbed species to be valid. Because standard hexane extraction removes both absorbed species and vitamin E from diffused vitamin E retrievals, the correlation method presented in this study is the recommended analysis alternative.

Low-dose multiple-information retrieval algorithm for X-ray grating-based imaging

International Nuclear Information System (INIS)

Wang Zhentian; Huang Zhifeng; Chen Zhiqiang; Zhang Li; Jiang Xiaolei; Kang Kejun; Yin Hongxia; Wang Zhenchang; Stampanoni, Marco

2011-01-01

The present work proposes a low dose information retrieval algorithm for X-ray grating-based multiple-information imaging (GB-MII) method, which can retrieve the attenuation, refraction and scattering information of samples by only three images. This algorithm aims at reducing the exposure time and the doses delivered to the sample. The multiple-information retrieval problem in GB-MII is solved by transforming a nonlinear equations set to a linear equations and adopting the nature of the trigonometric functions. The proposed algorithm is validated by experiments both on conventional X-ray source and synchrotron X-ray source, and compared with the traditional multiple-image-based retrieval algorithm. The experimental results show that our algorithm is comparable with the traditional retrieval algorithm and especially suitable for high Signal-to-Noise system.

Dictionary Pruning with Visual Word Significance for Medical Image Retrieval.

Science.gov (United States)

Zhang, Fan; Song, Yang; Cai, Weidong; Hauptmann, Alexander G; Liu, Sidong; Pujol, Sonia; Kikinis, Ron; Fulham, Michael J; Feng, David Dagan; Chen, Mei

2016-02-12

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.

Qualification of a Null Lens Using Image-Based Phase Retrieval

Science.gov (United States)

Bolcar, Matthew R.; Aronstein, David L.; Hill, Peter C.; Smith, J. Scott; Zielinski, Thomas P.

2012-01-01

In measuring the figure error of an aspheric optic using a null lens, the wavefront contribution from the null lens must be independently and accurately characterized in order to isolate the optical performance of the aspheric optic alone. Various techniques can be used to characterize such a null lens, including interferometry, profilometry and image-based methods. Only image-based methods, such as phase retrieval, can measure the null-lens wavefront in situ - in single-pass, and at the same conjugates and in the same alignment state in which the null lens will ultimately be used - with no additional optical components. Due to the intended purpose of a Dull lens (e.g., to null a large aspheric wavefront with a near-equal-but-opposite spherical wavefront), characterizing a null-lens wavefront presents several challenges to image-based phase retrieval: Large wavefront slopes and high-dynamic-range data decrease the capture range of phase-retrieval algorithms, increase the requirements on the fidelity of the forward model of the optical system, and make it difficult to extract diagnostic information (e.g., the system F/#) from the image data. In this paper, we present a study of these effects on phase-retrieval algorithms in the context of a null lens used in component development for the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission. Approaches for mitigation are also discussed.

Generating region proposals for histopathological whole slide image retrieval.

Science.gov (United States)

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu; Shi, Jun

2018-06-01

Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels. This paper presents a novel unsupervised region proposing method for histopathological WSI based on Selective Search. Specifically, the WSI is over-segmented into regions which are hierarchically merged until the WSI becomes a single region. Nucleus-oriented similarity measures for region mergence and Nucleus-Cytoplasm color space for histopathological image are specially defined to generate accurate region proposals. Additionally, we propose a new semi-supervised hashing method for image retrieval. The semantic features of images are extracted with Latent Dirichlet Allocation and transformed into binary hashing codes with Supervised Hashing. The methods are tested on a large-scale multi-class database of breast histopathological WSIs. The results demonstrate that for one WSI, our region proposing method can generate 7.3 thousand contoured regions which fit well with 95.8% of the ROIs annotated by pathologists. The proposed hashing method can retrieve a query image among 136 thousand images in 0.29 s and reach precision of 91% with only 10% of images labeled. The unsupervised region proposing method can generate regions as predictions of lesions in histopathological WSI. The region proposals can also serve as the training samples to train machine-learning models for image retrieval. The proposed hashing method can achieve fast and precise image retrieval with small amount of labels. Furthermore, the proposed methods can be potentially applied in online computer-aided-diagnosis systems. Copyright

Biomedical information retrieval across languages.

Science.gov (United States)

Daumke, Philipp; Markü, Kornél; Poprat, Michael; Schulz, Stefan; Klar, Rüdiger

2007-06-01

This work presents a new dictionary-based approach to biomedical cross-language information retrieval (CLIR) that addresses many of the general and domain-specific challenges in current CLIR research. Our method is based on a multilingual lexicon that was generated partly manually and partly automatically, and currently covers six European languages. It contains morphologically meaningful word fragments, termed subwords. Using subwords instead of entire words significantly reduces the number of lexical entries necessary to sufficiently cover a specific language and domain. Mediation between queries and documents is based on these subwords as well as on lists of word-n-grams that are generated from large monolingual corpora and constitute possible translation units. The translations are then sent to a standard Internet search engine. This process makes our approach an effective tool for searching the biomedical content of the World Wide Web in different languages. We evaluate this approach using the OHSUMED corpus, a large medical document collection, within a cross-language retrieval setting.

Learning Psychological Research and Statistical Concepts using Retrieval-based Practice

OpenAIRE

Stephen Wee Hun eLim; Gavin Jun Peng eNg; Gabriel Qi Hao eWong

2015-01-01

Research methods and statistics are an indispensable subject in the undergraduate psychology curriculum, but there are challenges associated with engaging students in it, such as making learning durable. Here we hypothesized that retrieval-based learning promotes long-term retention of statistical knowledge in psychology. Participants either studied the educational material in four consecutive periods, or studied it just once and practiced retrieving the information in the subsequent three pe...

A new software suite for NO{sub 2} vertical profile retrieval from ground-based zenith-sky spectrometers

Energy Technology Data Exchange (ETDEWEB)

Denis, L. [British Antarctic Survey/NERC, Madingley Road, Cambridge CB3 0ET (United Kingdom); Roscoe, H.K. [British Antarctic Survey/NERC, Madingley Road, Cambridge CB3 0ET (United Kingdom)]. E-mail: h.roscoe@bas.ac.uk; Chipperfield, M.P. [Environment Centre, University of Leeds, Leeds LS2 9JT (United Kingdom); Roozendael, M. van [Belgian Institute for Space Aeronomy (BIRA/IASB), 1180 Brussels (Belgium); Goutail, F. [Service d' Aeronomie du CNRS, BP3, 91271 Verrieres le Buisson (France)

2005-05-15

Here we present an operational method to improve accuracy and information content of ground-based measurements of stratospheric NO{sub 2}. The motive is to improve the investigation of trends in NO{sub 2}, and is important because the current trend in NO{sub 2} appears to contradict the trend in its source, suggesting that the stratospheric circulation has changed. To do so, a new software package for retrieving NO{sub 2} vertical profiles from slant columns measured by zenith-sky spectrometers has been created. It uses a Rodgers optimal linear inverse method coupled with a radiative transfer model for calculations of transfer functions between profiles and columns, and a chemical box model for taking into account the NO{sub 2} variations during twilight and during the day. Each model has parameters that vary according to season and location. Forerunners of each model have been previously validated. The scheme maps random errors in the measurements and systematic errors in the models and their parameters on to the retrieved profiles. Initialisation for models is derived from well-established climatologies. The software has been tested by comparing retrieved profiles to simultaneous balloon-borne profiles at mid-latitudes in spring.

The AMIDA automatic content linking device: Just-in-time document retrieval in meetings

NARCIS (Netherlands)

Popescu-Belis, A.; Boertjes, E.M.; Kilgour, J.; Poller, P.; Castronovo, S.; Wilson, T.; Jaimes, A.; Carletta, J.

2008-01-01

The AMIDA Automatic Content Linking Device (ACLD) is a just-in-time document retrieval system for meeting environments. The ACLD listens to a meeting and displays information about the documents from the group's history that are most relevant to what is being said. Participants can view an outline

Web information retrieval based on ontology

Science.gov (United States)

Zhang, Jian

2013-03-01

The purpose of the Information Retrieval (IR) is to find a set of documents that are relevant for a specific information need of a user. Traditional Information Retrieval model commonly used in commercial search engine is based on keyword indexing system and Boolean logic queries. One big drawback of traditional information retrieval is that they typically retrieve information without an explicitly defined domain of interest to the users so that a lot of no relevance information returns to users, which burden the user to pick up useful answer from these no relevance results. In order to tackle this issue, many semantic web information retrieval models have been proposed recently. The main advantage of Semantic Web is to enhance search mechanisms with the use of Ontology's mechanisms. In this paper, we present our approach to personalize web search engine based on ontology. In addition, key techniques are also discussed in our paper. Compared to previous research, our works concentrate on the semantic similarity and the whole process including query submission and information annotation.

A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval.

Science.gov (United States)

Cai, Jia; Tang, Yi

2018-02-01

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

An On-Demand Retrieval Method Based on Hybrid NoSQL for Multi-Layer Image Tiles in Disaster Reduction Visualization

Directory of Open Access Journals (Sweden)

Linyao Qiu

2017-01-01

Full Text Available Monitoring, response, mitigation and damage assessment of disasters places a wide variety of demands on the spatial and temporal resolutions of remote sensing images. Images are divided into tile pyramids by data sources or resolutions and published as independent image services for visualization. A disaster-affected area is commonly covered by multiple image layers to express hierarchical surface information, which generates a large amount of namesake tiles from different layers that overlay the same location. The traditional tile retrieval method for visualization cannot distinguish between distinct layers and traverses all image datasets for each tile query. This process produces redundant queries and invalid access that can seriously affect the visualization performance of clients, servers and network transmission. This paper proposes an on-demand retrieval method for multi-layer images and defines semantic annotations to enrich the description of each dataset. By matching visualization demands with the semantic information of datasets, this method automatically filters inappropriate layers and finds the most suitable layer for the final tile query. The design and implementation are based on a two-layer NoSQL database architecture that provides scheduling optimization and concurrent processing capability. The experimental results reflect the effectiveness and stability of the approach for multi-layer retrieval in disaster reduction visualization.

Some aspects of the file organization and retrieval strategy in large data-bases

International Nuclear Information System (INIS)

Arnaudov, D.D.; Govorun, N.N.

1977-01-01

Methods of organizing a big information retrieval system are discribed. A special attention is paid to the file organization. An adapting file structure is described in more detail. The discussed method gives one the opportunity to organize large files in such a way that the response time of the system can be minimized, when the file is increasing. In connection with the retrieval strategy a method is proposed, which uses the frequencies of the descr/iptors and the couples of the descriptors to forecast the expected number of the relevant documents. Programmes are made, on the base of these methods, which are used in the information retrieval systems of JINR

The effects of retrieval ease on health issue judgments: implications for campaign strategies.

Science.gov (United States)

Chang, Chingching

2010-12-01

This paper examines the effects of retrieving information about a health ailment on judgments of the perceived severity of the disease and self-efficacy regarding prevention and treatment. The literature on metacognition suggests that recall tasks render two types of information accessible: the retrieved content, and the subjective experience of retrieving the content. Both types of information can influence judgments. Content-based thinking models hold that the more instances of an event people can retrieve, the higher they will estimate the frequency of the event to be. In contrast, experience-based thinking models suggest that when people experience difficulty in retrieving information regarding an event, they rate the event as less likely to occur. In the first experiment, ease of retrieval was manipulated by asking participants to list either a high or low number of consequences of an ailment. As expected, retrieval difficulty resulted in lower perceived disease severity. In the second experiment, ease of retrieval was manipulated by varying the number of disease prevention or treatment measures participants attempted to list. As predicted, retrieval difficulty resulted in lower self-efficacy regarding prevention and treatment. In experiment three, when information regarding a health issue was made accessible by exposure to public service announcements (PSAs), ease-of-retrieval effects were attenuated. Finally, in experiment four, exposure to PSAs encouraged content-based judgments when the issue was of great concern.

A 1DVAR-based snowfall rate retrieval algorithm for passive microwave radiometers

Science.gov (United States)

Meng, Huan; Dong, Jun; Ferraro, Ralph; Yan, Banghua; Zhao, Limin; Kongoli, Cezar; Wang, Nai-Yu; Zavodsky, Bradley

2017-06-01

Snowfall rate retrieval from spaceborne passive microwave (PMW) radiometers has gained momentum in recent years. PMW can be so utilized because of its ability to sense in-cloud precipitation. A physically based, overland snowfall rate (SFR) algorithm has been developed using measurements from the Advanced Microwave Sounding Unit-A/Microwave Humidity Sounder sensor pair and the Advanced Technology Microwave Sounder. Currently, these instruments are aboard five polar-orbiting satellites, namely, NOAA-18, NOAA-19, Metop-A, Metop-B, and Suomi-NPP. The SFR algorithm relies on a separate snowfall detection algorithm that is composed of a satellite-based statistical model and a set of numerical weather prediction model-based filters. There are four components in the SFR algorithm itself: cloud properties retrieval, computation of ice particle terminal velocity, ice water content adjustment, and the determination of snowfall rate. The retrieval of cloud properties is the foundation of the algorithm and is accomplished using a one-dimensional variational (1DVAR) model. An existing model is adopted to derive ice particle terminal velocity. Since no measurement of cloud ice distribution is available when SFR is retrieved in near real time, such distribution is implicitly assumed by deriving an empirical function that adjusts retrieved SFR toward radar snowfall estimates. Finally, SFR is determined numerically from a complex integral. The algorithm has been validated against both radar and ground observations of snowfall events from the contiguous United States with satisfactory results. Currently, the SFR product is operationally generated at the National Oceanic and Atmospheric Administration and can be obtained from that organization.

Wave propagation method as an accurate technique for effective refractive index retrieving

DEFF Research Database (Denmark)

Andryieuski, Andrei; Malureanu, Radu; Lavrinenko, Andrei

2009-01-01

An effective parameters retrieval method based on the wave propagation simulation is proposed and compared with the standard S-parameter procedure. The method is free from possible mistakes originated by the multiple branching of solutions in the S-parameter procedure and shows high accuracy. The...

Leaf Surface Effects on Retrieving Chlorophyll Content from Hyperspectral Remote Sensing

Science.gov (United States)

Qiu, Feng; Chen, JingMing; Ju, Weimin; Wang, Jun; Zhang, Qian

2017-04-01

Light reflected directly from the leaf surface without entering the surface layer is not influenced by leaf internal biochemical content. Leaf surface reflectance varies from leaf to leaf due to differences in the surface roughness features and is relatively more important in strong absorption spectral regions. Therefore it introduces dispersion of data points in the relationship between biochemical concentration and reflectance (especially in the visible region). Separation of surface from total leaf reflection is important to improve the link between leaf pigments content and remote sensing data. This study aims to estimate leaf surface reflectance from hyperspectral remote sensing data and retrieve chlorophyll content by inverting a modified PROSPECT model. Considering leaf surface reflectance is almost the same in the visible and near infrared spectral regions, a surface layer with a reflectance independent of wavelength but varying from leaf to leaf was added to the PROSPECT model. The specific absorption coefficients of pigments were recalibrated. Then the modified model was inverted on independent datasets to check the performance of the model in predicting the chlorophyll content. Results show that differences in estimated surface layer reflectance of various species are noticeable. Surface reflectance of leaves with epicuticular waxes and trichomes is usually higher than other samples. Reconstruction of leaf reflectance and transmittance in the 400-1000 nm wavelength region using the modified PROSPECT model is excellent with low root mean square error (RMSE) and bias. Improvements for samples with high surface reflectance (e.g. maize) are significant, especially for high pigment leaves. Moreover, chlorophyll retrieved from inversion of the modified model is consequently improved (RMSE from 5.9-13.3 ug/cm2 with mean value 8.1 ug/cm2, while mean correlation coefficient is 0.90) compared to results of PROSPECT-5 (RMSE from 9.6-20.2 ug/cm2 with mean value 13

Water vapor retrieval from near-IR measurements of polarized scanning atmospheric corrector

Science.gov (United States)

Qie, Lili; Ning, Yuanming; Zhang, Yang; Chen, Xingfeng; Ma, Yan; Li, Zhengqiang; Cui, Wenyu

2018-02-01

Water vapor and aerosol are two key atmospheric factors effecting the remote sensing image quality. As water vapor is responsible for most of the solar radiation absorption occurring in the cloudless atmosphere, accurate measurement of water content is important to not only atmospheric correction of remote sensing images, but also many other applications such as the study of energy balance and global climate change, land surface temperature retrieval in thermal remote sensing. A multi-spectral, single-angular, polarized radiometer called Polarized Scanning Atmospheric Corrector (PSAC) were developed in China, which are designed to mount on the same satellite platform with the principle payload and provide essential parameters for principle payload image atmospheric correction. PSAC detect water vapor content via measuring atmosphere reflectance at water vapor absorbing channels (i.e. 0.91 μm) and nearby atmospheric window channel (i.e. 0.865μm). A near-IR channel ratio method was implemented to retrieve column water vapor (CWV) amount from PSAC measurements. Field experiments were performed at Yantai, in Shandong province of China, PSAC aircraft observations were acquired. The comparison between PSAC retrievals and ground-based Sun-sky radiometer measurements of CWV during the experimental flights illustrates that this method retrieves CWV with relative deviations ranging from 4% 13%. This method retrieve CWV more accurate over land than over ocean, as the water reflectance is low.

Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison

NARCIS (Netherlands)

Verrelst, Jochem; Rivera, Juan Pablo; Veroustraete, Frank; Muñoz-Marí, Jordi; Clevers, J.G.P.W.; Camps-Valls, Gustau; Moreno, José

2015-01-01

Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC),

A Framework Based on 2-D Taylor Expansion for Quantifying the Impacts of Subpixel Reflectance Variance and Covariance on Cloud Optical Thickness and Effective Radius Retrievals Based on the Bispectral Method

Science.gov (United States)

Zhang, Z.; Werner, F.; Cho, H.-M.; Wind, G.; Platnick, S.; Ackerman, A. S.; Di Girolamo, L.; Marshak, A.; Meyer, K.

2016-01-01

The bispectral method retrieves cloud optical thickness (t) and cloud droplet effective radius (re) simultaneously from a pair of cloud reflectance observations, one in a visible or near-infrared (VIS/NIR) band and the other in a shortwave infrared (SWIR) band. A cloudy pixel is usually assumed to be horizontally homogeneous in the retrieval. Ignoring subpixel variations of cloud reflectances can lead to a significant bias in the retrieved t and re. In the literature, the retrievals of t and re are often assumed to be independent and considered separately when investigating the impact of subpixel cloud reflectance variations on the bispectral method. As a result, the impact on t is contributed only by the subpixel variation of VIS/NIR band reflectance and the impact on re only by the subpixel variation of SWIR band reflectance. In our new framework, we use the Taylor expansion of a two-variable function to understand and quantify the impacts of subpixel variances of VIS/NIR and SWIR cloud reflectances and their covariance on the t and re retrievals. This framework takes into account the fact that the retrievals are determined by both VIS/NIR and SWIR band observations in a mutually dependent way. In comparison with previous studies, it provides a more comprehensive understanding of how subpixel cloud reflectance variations impact the t and re retrievals based on the bispectral method. In particular, our framework provides a mathematical explanation of how the subpixel variation in VIS/NIR band influences the re retrieval and why it can sometimes outweigh the influence of variations in the SWIR band and dominate the error in re retrievals, leading to a potential contribution of positive bias to the re retrieval. We test our framework using synthetic cloud fields from a large-eddy simulation and real observations from Moderate Resolution Imaging Spectroradiometer. The predicted results based on our framework agree very well with the numerical simulations. Our

Learning semantic and visual similarity for endomicroscopy video retrieval.

Science.gov (United States)

Andre, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2012-06-01

Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called "Dense-Sift," that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them

High-resolution methods for fluorescence retrieval from space

NARCIS (Netherlands)

Mazzoni, M.; Falorni, P.; Verhoef, W.

2010-01-01

The retrieval from space of a very weak fluorescence signal was studied in the O2A and O2B oxygen atmospheric absorption bands. The accuracy of the method was tested for the retrieval of the chlorophyll fluorescence and reflectance terms contributing to the sensor signal. The radiance at the top of

Diversification of visual media retrieval results using saliency detection

Science.gov (United States)

Muratov, Oleg; Boato, Giulia; De Natale, Franesco G. B.

2013-03-01

Diversification of retrieval results allows for better and faster search. Recently there has been proposed different methods for diversification of image retrieval results mainly utilizing text information and techniques imported from natural language processing domain. However, images contain visual information that is impossible to describe in text and the use of visual features is inevitable. Visual saliency is information about the main object of an image implicitly included by humans while creating visual content. For this reason it is naturally to exploit this information for the task of diversification of the content. In this work we study whether visual saliency can be used for the task of diversification and propose a method for re-ranking image retrieval results using saliency. The evaluation has shown that the use of saliency information results in higher diversity of retrieval results.

Three-dimensional ordered particulate structures: Method to retrieve characteristics from photonic band gap data

Science.gov (United States)

Miskevich, Alexander A.; Loiko, Valery A.

2015-01-01

A method to retrieve characteristics of ordered particulate structures, such as photonic crystals, is proposed. It is based on the solution of the inverse problem using data on the photonic band gap (PBG). The quasicrystalline approximation (QCA) of the theory of multiple scattering of waves and the transfer matrix method (TMM) are used. Retrieval of the refractive index of particles is demonstrated. Refractive indices of the artificial opal particles are estimated using the published experimental data.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm.

Science.gov (United States)

Yang, Mengzhao; Song, Wei; Mei, Haibin

2017-07-23

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

Standards-based Content Resources: A Prerequisite for Content Integration and Content Interoperability

Directory of Open Access Journals (Sweden)

Christian Galinski

2010-05-01

Full Text Available Objective: to show how standards-based approaches for content standardization, content management, content related services and tools as well as the respective certification systems not only guarantee reliable content integration and content interoperability, but also are of particular benefit to people with special needs in eAccessibility/eInclusion. Method: document MoU/MG/05 N0221 ''Semantic Interoperability and the need for a coherent policy for a framework of distributed, possibly federated repositories for all kinds of content items on a world-wide scale''2, which was adopted in 2005, was a first step towards the formulation of global interoperability requirements for structured content. These requirements -based on advanced terminological principles- were taken up in EU-projects such as IN-SAFETY (INfrastructure and SAFETY and OASIS (Open architecture for Accessible Services Integration and Standardization. Results: Content integration and content interoperability are key concepts in connection with the emergence of state-of-the-art distributed and federated databases/repositories of structured content. Given the fact that linguistic content items are increasingly combined with or embedded in non-linguistic content items (and vice versa, a systemic and generic approach to data modelling and content management has become the order of the day. Fulfilling the requirements of capability for multilinguality and multimodality, based on open standards makes software and database design fit for eAccessibility/eInclusion from the outset. It also makes structured content capable for global content integration and content interoperability, because it enhances its potential for being re-used and re-purposed in totally different eApplications. Such content as well as the methods, tools and services applied can be subject to new kinds of certification schemes which also should be based on standards. Conclusions: Content must be totally reliable in some

Retrieval method of aerosol extinction coefficient profile based on backscattering, side-scattering and Raman-scattering lidar

Science.gov (United States)

Shan, Huihui; Zhang, Hui; Liu, Junjian; Tao, Zongming; Wang, Shenhao; Ma, Xiaomin; Zhou, Pucheng; Yao, Ling; Liu, Dong; Xie, Chenbo; Wang, Yingjian

2018-03-01

Aerosol extinction coefficient profile is an essential parameter for atmospheric radiation model. It is difficult to get higher signal to noise ratio (SNR) of backscattering lidar from the ground to the tropopause especially in near range. Higher SNR problem can be solved by combining side-scattering and backscattering lidar. Using Raman-scattering lidar, aerosol extinction to backscatter ratio (lidar ratio) can be got. Based on side-scattering, backscattering and Raman-scattering lidar system, aerosol extinction coefficient is retrieved precisely from the earth's surface to the tropopause. Case studies show this method is reasonable and feasible.

Parapsychology and the neurosciences: a computer-based content analysis of abstracts in the database "MEDLINE" from 1975 to 1995.

Science.gov (United States)

Fassbender, P

1997-04-01

A computer-based content of 109 abstracts retrieved by the subject heading "parapsychology" from the database MEDLINE for the years 1975-1995 is presented. Data were analyzed by four categories to terms denoting (1) research methods, (2) neurosciences, (3) humanities/psychodynamics, and (4) parapsychology. Results indicated a growing interest in neuroscientific and neuropsychological explanations and theories.

Multi-clues image retrieval based on improved color invariants

Science.gov (United States)

Liu, Liu; Li, Jian-Xun

2012-05-01

At present, image retrieval has a great progress in indexing efficiency and memory usage, which mainly benefits from the utilization of the text retrieval technology, such as the bag-of-features (BOF) model and the inverted-file structure. Meanwhile, because the robust local feature invariants are selected to establish BOF, the retrieval precision of BOF is enhanced, especially when it is applied to a large-scale database. However, these local feature invariants mainly consider the geometric variance of the objects in the images, and thus the color information of the objects fails to be made use of. Because of the development of the information technology and Internet, the majority of our retrieval objects is color images. Therefore, retrieval performance can be further improved through proper utilization of the color information. We propose an improved method through analyzing the flaw of shadow-shading quasi-invariant. The response and performance of shadow-shading quasi-invariant for the object edge with the variance of lighting are enhanced. The color descriptors of the invariant regions are extracted and integrated into BOF based on the local feature. The robustness of the algorithm and the improvement of the performance are verified in the final experiments.

Ad-hoc Content-based Queries and Data Analysis for Virtual Observatories, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — Aquilent, Inc. proposes to support ad-hoc, content-based query and data retrieval from virtual observatories (VxO) by developing 1) Higher Order Query Services that...

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

Directory of Open Access Journals (Sweden)

Perez-Rey David

2012-04-01

Full Text Available Abstract Background Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs. In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA, (ii identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH, automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

Directory of Open Access Journals (Sweden)

Minh-Tan Pham

2017-10-01

Full Text Available A novel efficient method for content-based image retrieval (CBIR is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

Directory of Open Access Journals (Sweden)

Rosa Oltra-Carrió

2015-03-01

Full Text Available The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed and SWIR (ShortWave InfraRed regions (from 0.4 to 2.5 µm when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON. These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R2 between 0.72 and 0.92. A clay content (CC dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC. The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² superior to 0.76 and Root mean square errors inferior to 8.3% m3∙m−3 in all cases and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH.

Toward privacy-preserving JPEG image retrieval

Science.gov (United States)

Cheng, Hang; Wang, Jingyue; Wang, Meiqing; Zhong, Shangping

2017-07-01

This paper proposes a privacy-preserving retrieval scheme for JPEG images based on local variance. Three parties are involved in the scheme: the content owner, the server, and the authorized user. The content owner encrypts JPEG images for privacy protection by jointly using permutation cipher and stream cipher, and then, the encrypted versions are uploaded to the server. With an encrypted query image provided by an authorized user, the server may extract blockwise local variances in different directions without knowing the plaintext content. After that, it can calculate the similarity between the encrypted query image and each encrypted database image by a local variance-based feature comparison mechanism. The authorized user with the encryption key can decrypt the returned encrypted images with plaintext content similar to the query image. The experimental results show that the proposed scheme not only provides effective privacy-preserving retrieval service but also ensures both format compliance and file size preservation for encrypted JPEG images.

Connectionist Interaction Information Retrieval.

Science.gov (United States)

Dominich, Sandor

2003-01-01

Discussion of connectionist views for adaptive clustering in information retrieval focuses on a connectionist clustering technique and activation spreading-based information retrieval model using the interaction information retrieval method. Presents theoretical as well as simulation results as regards computational complexity and includes…

Wave propagation retrieval method for chiral metamaterials

DEFF Research Database (Denmark)

Andryieuski, Andrei; Malureanu, Radu; Lavrinenko, Andrei

2010-01-01

In this paper we present the wave propagation method for the retrieving of effective properties of media with circularly polarized eigenwaves, in particularly for chiral metamaterials. The method is applied for thick slabs and provides bulk effective parameters. Its strong sides are the absence...

Physical retrieval of precipitation water contents from Special Sensor Microwave/Imager (SSM/I) data. Part 1: A cloud ensemble/radiative parameterization for sensor response (report version)

Science.gov (United States)

Olson, William S.; Raymond, William H.

1990-01-01

The physical retrieval of geophysical parameters based upon remotely sensed data requires a sensor response model which relates the upwelling radiances that the sensor observes to the parameters to be retrieved. In the retrieval of precipitation water contents from satellite passive microwave observations, the sensor response model has two basic components. First, a description of the radiative transfer of microwaves through a precipitating atmosphere must be considered, because it is necessary to establish the physical relationship between precipitation water content and upwelling microwave brightness temperature. Also the spatial response of the satellite microwave sensor (or antenna pattern) must be included in the description of sensor response, since precipitation and the associated brightness temperature field can vary over a typical microwave sensor resolution footprint. A 'population' of convective cells, as well as stratiform clouds, are simulated using a computationally-efficient multi-cylinder cloud model. Ensembles of clouds selected at random from the population, distributed over a 25 km x 25 km model domain, serve as the basis for radiative transfer calculations of upwelling brightness temperatures at the SSM/I frequencies. Sensor spatial response is treated explicitly by convolving the upwelling brightness temperature by the domain-integrated SSM/I antenna patterns. The sensor response model is utilized in precipitation water content retrievals.

The influence of learning methods on collaboration: prior repeated retrieval enhances retrieval organization, abolishes collaborative inhibition, and promotes post-collaborative memory.

Science.gov (United States)

Congleton, Adam R; Rajaram, Suparna

2011-11-01

Research on collaborative memory has unveiled the counterintuitive yet robust phenomenon that collaboration impairs group recall. A candidate explanation for this collaborative inhibition effect is the disruption of people's idiosyncratic retrieval strategies during collaboration, and it is hypothesized that employing methods that improve one's organization protects against retrieval disruption. Here it is investigated how one's learning method during the study phase--defined as either repeatedly studying or repeatedly retrieving information--influences retrieval organization and what effects this has on collaborative recall and post-collaborative individual recall. Results show that repeated retrieval consistently eliminated collaborative inhibition. This enabled participants to gain the most from re-exposure to materials recalled by their partners that they themselves did not recall and led to improvements in their individual memory following collaboration. This repeated retrieval advantage stemmed from the preferential manner in which this learning method strengthened retrieval organization. Findings are also discussed that reveal a relationship between retrieval organization and the interaction observed between learning method and short versus long delay seen in the testing effect literature. Finally, results show that the elusive benefits of cross-cuing during collaboration may be best detected with a longer study-test delay. Together, these findings illuminate when and how collaboration can enhance memory.

Computer-aided diagnosis of mammographic masses using geometric verification-based image retrieval

Science.gov (United States)

Li, Qingliang; Shi, Weili; Yang, Huamin; Zhang, Huimao; Li, Guoxin; Chen, Tao; Mori, Kensaku; Jiang, Zhengang

2017-03-01

Computer-Aided Diagnosis of masses in mammograms is an important indicator of breast cancer. The use of retrieval systems in breast examination is increasing gradually. In this respect, the method of exploiting the vocabulary tree framework and the inverted file in the mammographic masse retrieval have been proved high accuracy and excellent scalability. However it just considered the features in each image as a visual word and had ignored the spatial configurations of features. It greatly affect the retrieval performance. To overcome this drawback, we introduce the geometric verification method to retrieval in mammographic masses. First of all, we obtain corresponding match features based on the vocabulary tree framework and the inverted file. After that, we grasps the main point of local similarity characteristic of deformations in the local regions by constructing the circle regions of corresponding pairs. Meanwhile we segment the circle to express the geometric relationship of local matches in the area and generate the spatial encoding strictly. Finally we judge whether the matched features are correct or not, based on verifying the all spatial encoding are whether satisfied the geometric consistency. Experiments show the promising results of our approach.

A Framework Based on 2-D Taylor Expansion for Quantifying the Impacts of Sub-Pixel Reflectance Variance and Covariance on Cloud Optical Thickness and Effective Radius Retrievals Based on the Bi-Spectral Method

Science.gov (United States)

Zhang, Z.; Werner, F.; Cho, H. -M.; Wind, G.; Platnick, S.; Ackerman, A. S.; Di Girolamo, L.; Marshak, A.; Meyer, Kerry

2016-01-01

The bi-spectral method retrieves cloud optical thickness and cloud droplet effective radius simultaneously from a pair of cloud reflectance observations, one in a visible or near-infrared (VISNIR) band and the other in a shortwave infrared (SWIR) band. A cloudy pixel is usually assumed to be horizontally homogeneous in the retrieval. Ignoring sub-pixel variations of cloud reflectances can lead to a significant bias in the retrieved and re. In the literature, the retrievals of and re are often assumed to be independent and considered separately when investigating the impact of sub-pixel cloud reflectance variations on the bi-spectral method. As a result, the impact on is contributed only by the sub-pixel variation of VISNIR band reflectance and the impact on re only by the sub-pixel variation of SWIR band reflectance. In our new framework, we use the Taylor expansion of a two-variable function to understand and quantify the impacts of sub-pixel variances of VISNIR and SWIR cloud reflectances and their covariance on the and re retrievals. This framework takes into account the fact that the retrievals are determined by both VISNIR and SWIR band observations in a mutually dependent way. In comparison with previous studies, it provides a more comprehensive understanding of how sub-pixel cloud reflectance variations impact the and re retrievals based on the bi-spectral method. In particular, our framework provides a mathematical explanation of how the sub-pixel variation in VISNIR band influences the re retrieval and why it can sometimes outweigh the influence of variations in the SWIR band and dominate the error in re retrievals, leading to a potential contribution of positive bias to the re retrieval. We test our framework using synthetic cloud fields from a large-eddy simulation and real observations from Moderate Resolution Imaging Spectroradiometer. The predicted results based on our framework agree very well with the numerical simulations. Our framework can be used

A Fuzzy Color-Based Approach for Understanding Animated Movies Content in the Indexing Task

Directory of Open Access Journals (Sweden)

Vasile Buzuloiu

2008-04-01

Full Text Available This paper proposes a method for detecting and analyzing the color techniques used in the animated movies. Each animated movie uses a specific color palette which makes its color distribution one major feature in analyzing the movie content. The color palette is specially tuned by the author in order to convey certain feelings or to express artistic concepts. Deriving semantic or symbolic information from the color concepts or the visual impression induced by the movie should be an ideal way of accessing its content in a content-based retrieval system. The proposed approach is carried out in two steps. The first processing step is the low-level analysis. The movie color content gets represented with several global statistical parameters computed from the movie global weighted color histogram. The second step is the symbolic representation of the movie content. The numerical parameters obtained from the first step are converted into meaningful linguistic concepts through a fuzzy system. They concern mainly the predominant hues of the movie, some of Itten’s color contrasts and harmony schemes, color relationships and color richness. We use the proposed linguistic concepts to link to given animated movies according to their color techniques. In order to make the retrieval task easier, we also propose to represent color properties in a graphical manner which is similar to the color gamut representation. Several tests have been conducted on an animated movie database.

Simultenious binary hash and features learning for image retrieval

Science.gov (United States)

Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

2016-05-01

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

Modelling of chromatic contrast for retrieval of wallpaper images

OpenAIRE

Gao, Xiaohong W.; Wang, Yuanlei; Qian, Yu; Gao, Alice

2015-01-01

Colour remains one of the key factors in presenting an object and consequently has been widely applied in retrieval of images based on their visual contents. However, a colour appearance changes with the change of viewing surroundings, the phenomenon that has not been paid attention yet while performing colour-based image retrieval. To comprehend this effect, in this paper, a chromatic contrast model, CAMcc, is developed for the application of retrieval of colour intensive images, cementing t...

Pulse Retrieval Algorithm for Interferometric Frequency-Resolved Optical Gating Based on Differential Evolution

OpenAIRE

Hyyti, Janne; Escoto, Esmerando; Steinmeyer, Günter

2017-01-01

A novel algorithm for the ultrashort laser pulse characterization method of interferometric frequency-resolved optical gating (iFROG) is presented. Based on a genetic method, namely differential evolution, the algorithm can exploit all available information of an iFROG measurement to retrieve the complex electric field of a pulse. The retrieval is subjected to a series of numerical tests to prove robustness of the algorithm against experimental artifacts and noise. These tests show that the i...

An Effective Combined Feature For Web Based Image Retrieval

Directory of Open Access Journals (Sweden)

H.M.R.B Herath

2015-08-01

Full Text Available Abstract Technology advances as well as the emergence of large scale multimedia applications and the revolution of the World Wide Web has changed the world into a digital age. Anybody can use their mobile phone to take a photo at any time anywhere and upload that image to ever growing image databases. Development of effective techniques for visual and multimedia retrieval systems is one of the most challenging and important directions of the future research. This paper proposes an effective combined feature for web based image retrieval. Frequently used colour and texture features are explored in order to develop a combined feature for this purpose. Widely used three colour features Colour moments Colour coherence vector and Colour Correlogram and three texture features Grey Level Co-occurrence matrix Tamura features and Gabor filter were analyzed for their performance. Precision and Recall were used to evaluate the performance of each of these techniques. By comparing precision and recall values the methods that performed best were taken and combined to form a hybrid feature. The developed combined feature was evaluated by developing a web based CBIR system. A web crawler was used to first crawl through Web sites and images found in those sites are downloaded and the combined feature representation technique was used to extract image features. The test results indicated that this web system can be used to index web images with the combined feature representation schema and to find similar images. Random image retrievals using the web system shows that the combined feature can be used to retrieve images belonging to the general image domain. Accuracy of the retrieval can be noted high for natural images like outdoor scenes images of flowers etc. Also images which have a similar colour and texture distribution were retrieved as similar even though the images were belonging to deferent semantic categories. This can be ideal for an artist who wants

Information content analysis: the potential for methane isotopologue retrieval from GOSAT-2

Science.gov (United States)

Malina, Edward; Yoshida, Yukio; Matsunaga, Tsuneo; Muller, Jan-Peter

2018-02-01

Atmospheric methane is comprised of multiple isotopic molecules, with the most abundant being 12CH4 and 13CH4, making up 98 and 1.1 % of atmospheric methane respectively. It has been shown that is it possible to distinguish between sources of methane (biogenic methane, e.g. marshland, or abiogenic methane, e.g. fracking) via a ratio of these main methane isotopologues, otherwise known as the δ13C value. δ13C values typically range between -10 and -80 ‰, with abiogenic sources closer to zero and biogenic sources showing more negative values. Initially, we suggest that a δ13C difference of 10 ‰ is sufficient, in order to differentiate between methane source types, based on this we derive that a precision of 0.2 ppbv on 13CH4 retrievals may achieve the target δ13C variance. Using an application of the well-established information content analysis (ICA) technique for assumed clear-sky conditions, this paper shows that using a combination of the shortwave infrared (SWIR) bands on the planned Greenhouse gases Observing SATellite (GOSAT-2) mission, 13CH4 can be measured with sufficient information content to a precision of between 0.7 and 1.2 ppbv from a single sounding (assuming a total column average value of 19.14 ppbv), which can then be reduced to the target precision through spatial and temporal averaging techniques. We therefore suggest that GOSAT-2 can be used to differentiate between methane source types. We find that large unconstrained covariance matrices are required in order to achieve sufficient information content, while the solar zenith angle has limited impact on the information content.

Learning Psychological Research and Statistical Concepts using Retrieval-based Practice

Directory of Open Access Journals (Sweden)

Stephen Wee Hun eLim

2015-10-01

Full Text Available Research methods and statistics are an indispensable subject in the undergraduate psychology curriculum, but there are challenges associated with teaching it, such as making learning durable. Here we hypothesized that retrieval-based learning promotes long-term retention of statistical knowledge in psychology. Participants either studied the educational material in four consecutive periods, or studied it just once and practised retrieving the information in the subsequent three periods, and then took a final test through which their learning was assessed. Whereas repeated studying yielded better test performance when the final test was immediately administered, repeated practice yielded better performance when the test was administered a week after. The data suggest that retrieval practice enhanced the learning – produced better long-term retention – of statistical knowledge in psychology than did repeated studying.

A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF.

Directory of Open Access Journals (Sweden)

Nouman Ali

Full Text Available With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR, high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT and Speeded-Up Robust Features (SURF. The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.

Information retrieval system based on INIS tapes

International Nuclear Information System (INIS)

Pultorak, G.

1976-01-01

An information retrieval system based on the INIS computer tapes is described. It includes the three main elements of a computerized information system: a data base on a machine -readable medium, a collection of queries which represent the information needs from the data - base, and a set of programs by which the actual retrieval is done, according to the user's queries. The system is built for the center's computer, a CDC 3600, and its special features characterize, to a certain degree, the structure of the programs. (author)

Content Based Retrieval Database Management System with Support for Similarity Searching and Query Refinement

National Research Council Canada - National Science Library

Ortega-Binderberger, Michael

2002-01-01

... as a critical area of research. This thesis explores how to enhance database systems with content based search over arbitrary abstract data types in a similarity based framework with query refinement...

Alternating direction methods for classical and ptychographic phase retrieval

International Nuclear Information System (INIS)

Wen, Zaiwen; Yang, Chao; Liu, Xin; Marchesini, Stefano

2012-01-01

In this paper, we show how the augmented Lagrangian alternating direction method (ADM) can be used to solve both the classical and ptychographic phase retrieval problems. We point out the connection between ADM and projection algorithms such as the hybrid input–output algorithm, and compare its performance against standard algorithms for phase retrieval on a number of test images. Our computational experiments show that ADM appears to be less sensitive to the choice of relaxation parameters, and it usually outperforms the existing techniques for both the classical and ptychographic phase retrieval problems. (paper)

A Framework for Quantifying the Impacts of Sub-Pixel Reflectance Variance and Covariance on Cloud Optical Thickness and Effective Radius Retrievals Based on the Bi-Spectral Method.

Science.gov (United States)

Zhang, Z; Werner, F.; Cho, H. -M.; Wind, Galina; Platnick, S.; Ackerman, A. S.; Di Girolamo, L.; Marshak, A.; Meyer, Kerry

2017-01-01

The so-called bi-spectral method retrieves cloud optical thickness (t) and cloud droplet effective radius (re) simultaneously from a pair of cloud reflectance observations, one in a visible or near infrared (VIS/NIR) band and the other in a shortwave-infrared (SWIR) band. A cloudy pixel is usually assumed to be horizontally homogeneous in the retrieval. Ignoring sub-pixel variations of cloud reflectances can lead to a significant bias in the retrieved t and re. In this study, we use the Taylor expansion of a two-variable function to understand and quantify the impacts of sub-pixel variances of VIS/NIR and SWIR cloud reflectances and their covariance on the t and re retrievals. This framework takes into account the fact that the retrievals are determined by both VIS/NIR and SWIR band observations in a mutually dependent way. In comparison with previous studies, it provides a more comprehensive understanding of how sub-pixel cloud reflectance variations impact the t and re retrievals based on the bi-spectral method. In particular, our framework provides a mathematical explanation of how the sub-pixel variation in VIS/NIR band influences the re retrieval and why it can sometimes outweigh the influence of variations in the SWIR band and dominate the error in re retrievals, leading to a potential contribution of positive bias to the re retrieval.

A three-dimensional polarization domain retrieval method from electron diffraction data

International Nuclear Information System (INIS)

Pennington, Robert S.; Koch, Christoph T.

2015-01-01

We present an algorithm for retrieving three-dimensional domains of picometer-scale shifts in atomic positions from electron diffraction data, and apply it to simulations of ferroelectric polarization in BaTiO 3 . Our algorithm successfully and correctly retrieves polarization domains in which the Ti atom positions differ by less than 3 pm (0.4% of the unit cell diagonal distance) with 5 and 10 nm depth resolution along the beam direction, and we also retrieve unit cell strain, corresponding to tetragonal-to-cubic unit cell distortions, for 10 nm domains. Experimental applicability is also discussed. - Highlights: • We show a retrieval method for ferroelectric polarization from TEM diffraction data. • Simulated strain and polarization variations along the beam direction are retrieved. • This method can be used for 3D strain and polarization mapping without specimen tilt

Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features

Directory of Open Access Journals (Sweden)

Keranmu Xielifuguli

2014-01-01

Full Text Available People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images.

Information operator approach applied to the retrieval of vertical distributions of atmospheric constituents from ground-based FTIR measurements

Science.gov (United States)

Senten, Cindy; de Mazière, Martine; Vanhaelewyn, Gauthier; Vigouroux, Corinne; Delmas, Robert

2010-05-01

The retrieval of information about the vertical distribution of an atmospheric absorber from high spectral resolution ground-based Fourier Transform infrared (FTIR) solar absorption spectra is an important issue in remote sensing. A frequently used technique at present is the optimal estimation method. This work introduces the application of an alternative method, namely the information operator approach (Doicu et al., 2007; Hoogen et al., 1999), for extracting the available information from such FTIR measurements. This approach has been implemented within the well-known retrieval code SFIT2, by adapting the optimal estimation method such as to take into account only the significant contributions to the solution. In particular, we demonstrate the feasibility of the method when applied to ground-based FTIR spectra taken at the southern (sub)tropical site Ile de La Réunion (21° S, 55° E) in 2007. A thorough comparison has been made between the retrieval results obtained with the original optimal estimation method and the ones obtained with the information operator approach, regarding profile and column stability, information content and corresponding full error budget evaluation. This has been done for the target species ozone (O3), methane (CH4), nitrous oxide (N2O), and carbon monoxide (CO). It is shown that the information operator approach performs well and is capable of achieving the same accuracy as optimal estimation, with a gain of stability and with the additional advantage of being less sensitive to the choice of a priori information as well as to the actual signal-to-noise ratio. Keywords: ground-based FTIR, solar absorption spectra, greenhouse gases, information operator approach References Doicu, A., Hilgers, S., von Bargen, A., Rozanov, A., Eichmann, K.-U., von Savigny, C., and Burrows, J.P.: Information operator approach and iterative regularization methods for atmospheric remote sensing, J. Quant. Spectrosc. Radiat. Transfer, 103, 340-350, 2007

Chiral retrieval method based on right circularly polarized and left circularly polarized waves

International Nuclear Information System (INIS)

Martín, Ernesto; Muñoz, Juan; Margineda, José; Molina-Cuberos, Gregorio J; García-Collado, Ángel J

2014-01-01

The free-wave characterization of metamaterials is usually carried out by illuminating a sample with a linearly polarized plane electromagnetic wave. At points before and after the sample, sensors are introduced to measure the transverse components of the field, in order to compute the reflection and transmission coefficients related with the co- and cross-polar field components. Based on this information, retrieval algorithms allow parameters like rotation angle, effective chirality and refraction index to be calculated. Here we propose to use the transmission signals under illumination with plane circularly polarized waves, without sensing the reflection signal, to calculate the chirality parameter and the rotation angle due to the electromagnetic activity of the material. This new method, which allows a simpler characterization of a chiral slab, is applied to the study of metamaterials composed of both periodic and random distributions of metallic structures with chiral symmetry. The experimental results are contrasted with simulations and alternative measurements obtained using linearly polarized waves. (paper)

A Probabilistic Framework for Content-Based Diagnosis of Retinal Disease

Energy Technology Data Exchange (ETDEWEB)

Tobin Jr, Kenneth William [ORNL; Abdelrahman, Mohamed A [ORNL; Chaum, Edward [ORNL; Muthusamy Govindasamy, Vijaya Priya [ORNL; Karnowski, Thomas Paul [ORNL

2007-01-01

Diabetic retinopathy is the leading cause of blindness in the working age population around the world. Computer assisted analysis has the potential to assist in the early detection of diabetes by regular screening of large populations. The widespread availability of digital fundus cameras today is resulting in the accumulation of large image archives of diagnosed patient data that captures historical knowledge of retinal pathology. Through this research we are developing a content-based image retrieval method to verify our hypothesis that retinal pathology can be identified and quantified from visually similar retinal images in an image archive. We will present diagnostic results for specificity and sensitivity on a population of 395 fundus images representing the normal fundus and 14 stratified disease states.

A Method of Retrieving BRDF from Surface-Reflected Radiance Using Decoupling of Atmospheric Radiative Transfer and Surface Reflection

Directory of Open Access Journals (Sweden)

Alexander Radkevich

2018-04-01

Full Text Available Bi-directional reflection distribution function (BRDF defines anisotropy of the surface reflection. It is required to specify the boundary condition for radiative transfer (RT modeling used in aerosol retrievals, cloud retrievals, atmospheric modeling, and other applications. Ground based measurements of reflected radiance draw increasing attention as a source of information about anisotropy of surface reflection. Derivation of BRDF from surface radiance requires atmospheric correction. This study develops a new method of retrieving BRDF on its whole domain, making it immediately suitable for further atmospheric RT modeling applications. The method is based on the integral equation relating surface-reflected radiance, BRDF, and solutions of two auxiliary atmosphere-only RT problems. The method requires kernel-based BRDF. The weights of the kernels are obtained with a quickly converging iterative procedure. RT modeling has to be done only one time before the start of iterative process.

Method of and System for Information Retrieval

DEFF Research Database (Denmark)

2015-01-01

This invention relates to a system for and a method (100) of searching a collection of digital information (150) comprising a number of digital documents (110), the method comprising receiving or obtaining (102) a search query, the query comprising a number of search terms, searching (103) an ind......, a method of and a system for information retrieval or searching is readily provided that enhances the searching quality (i.e. the number of relevant documents retrieved and such documents being ranked high) when (also) using queries containing many search terms.......This invention relates to a system for and a method (100) of searching a collection of digital information (150) comprising a number of digital documents (110), the method comprising receiving or obtaining (102) a search query, the query comprising a number of search terms, searching (103) an index...... (300) using the search terms thereby providing information (301) about which digital documents (110) of the collection of digital information (150) that contains a given search term and one or more search related metrics (302; 303; 304; 305; 306), ranking (105) at least a part of the search result...

Mammogram retrieval through machine learning within BI-RADS standards.

Science.gov (United States)

Wei, Chia-Hung; Li, Yue; Huang, Pai Jung

2011-08-01

A content-based mammogram retrieval system can support usual comparisons made on images by physicians, answering similarity queries over images stored in the database. The importance of searching for similar mammograms lies in the fact that physicians usually try to recall similar cases by seeking images that are pathologically similar to a given image. This paper presents a content-based mammogram retrieval system, which employs a query example to search for similar mammograms in the database. In this system the mammographic lesions are interpreted based on their medical characteristics specified in the Breast Imaging Reporting and Data System (BI-RADS) standards. A hierarchical similarity measurement scheme based on a distance weighting function is proposed to model user's perception and maximizes the effectiveness of each feature in a mammographic descriptor. A machine learning approach based on support vector machines and user's relevance feedback is also proposed to analyze the user's information need in order to retrieve target images more accurately. Experimental results demonstrate that the proposed machine learning approach with Radial Basis Function (RBF) kernel function achieves the best performance among all tested ones. Furthermore, the results also show that the proposed learning approach can improve retrieval performance when applied to retrieve mammograms with similar mass and calcification lesions, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

Transformation invariant image indexing and retrieval for image databases

NARCIS (Netherlands)

Gevers, Th.; Smeulders, A.W.M.

1994-01-01

This paper presents a novel design of an image database system which supports storage, indexing and retrieval of images by content. The image retrieval methodology is based on the observation that images can be discriminated by the presence of image objects and their spatial relations. Images in the

Information content analysis: the potential for methane isotopologue retrieval from GOSAT-2

Directory of Open Access Journals (Sweden)

E. Malina

2018-02-01

Full Text Available Atmospheric methane is comprised of multiple isotopic molecules, with the most abundant being 12CH4 and 13CH4, making up 98 and 1.1 % of atmospheric methane respectively. It has been shown that is it possible to distinguish between sources of methane (biogenic methane, e.g. marshland, or abiogenic methane, e.g. fracking via a ratio of these main methane isotopologues, otherwise known as the δ13C value. δ13C values typically range between −10 and −80 ‰, with abiogenic sources closer to zero and biogenic sources showing more negative values. Initially, we suggest that a δ13C difference of 10 ‰ is sufficient, in order to differentiate between methane source types, based on this we derive that a precision of 0.2 ppbv on 13CH4 retrievals may achieve the target δ13C variance. Using an application of the well-established information content analysis (ICA technique for assumed clear-sky conditions, this paper shows that using a combination of the shortwave infrared (SWIR bands on the planned Greenhouse gases Observing SATellite (GOSAT-2 mission, 13CH4 can be measured with sufficient information content to a precision of between 0.7 and 1.2 ppbv from a single sounding (assuming a total column average value of 19.14 ppbv, which can then be reduced to the target precision through spatial and temporal averaging techniques. We therefore suggest that GOSAT-2 can be used to differentiate between methane source types. We find that large unconstrained covariance matrices are required in order to achieve sufficient information content, while the solar zenith angle has limited impact on the information content.

Cluster-based query expansion using external collections in medical information retrieval.

Science.gov (United States)

Oh, Heung-Seon; Jung, Yuchul

2015-12-01

Utilizing external collections to improve retrieval performance is challenging research because various test collections are created for different purposes. Improving medical information retrieval has also gained much attention as various types of medical documents have become available to researchers ever since they started storing them in machine processable formats. In this paper, we propose an effective method of utilizing external collections based on the pseudo relevance feedback approach. Our method incorporates the structure of external collections in estimating individual components in the final feedback model. Extensive experiments on three medical collections (TREC CDS, CLEF eHealth, and OHSUMED) were performed, and the results were compared with a representative expansion approach utilizing the external collections to show the superiority of our method. Copyright © 2015 Elsevier Inc. All rights reserved.

Description logic-based methods for auditing frame-based medical terminological systems.

Science.gov (United States)

Cornet, Ronald; Abu-Hanna, Ameen

2005-07-01

Medical terminological systems (TSs) play an increasingly important role in health care by supporting recording, retrieval and analysis of patient information. As the size and complexity of TSs are growing, the need arises for means to audit them, i.e. verify and maintain (logical) consistency and (semantic) correctness of their contents. This is not only important for the management of TSs but also for providing their users with confidence about the reliability of their contents. Formal methods have the potential to play an important role in the audit of TSs, although there are few empirical studies to assess the benefits of using these methods. In this paper we propose a method based on description logics (DLs) for the audit of TSs. This method is based on the migration of the medical TS from a frame-based representation to a DL-based one. Our method is characterized by a process in which initially stringent assumptions are made about concept definitions. The assumptions allow the detection of concepts and relations that might comprise a source of logical inconsistency. If the assumptions hold then definitions are to be altered to eliminate the inconsistency, otherwise the assumptions are revised. In order to demonstrate the utility of the approach in a real-world case study we audit a TS in the intensive care domain and discuss decisions pertaining to building DL-based representations. This case study demonstrates that certain types of inconsistencies can indeed be detected by applying the method to a medical terminological system. The added value of the method described in this paper is that it provides a means to evaluate the compliance to a number of common modeling principles in a formal manner. The proposed method reveals potential modeling inconsistencies, helping to audit and (if possible) improve the medical TS. In this way, it contributes to providing confidence in the contents of the terminological system.

Image Retrieval Algorithm Based on Discrete Fractional Transforms

Science.gov (United States)

Jindal, Neeru; Singh, Kulbir

2013-06-01

The discrete fractional transforms is a signal processing tool which suggests computational algorithms and solutions to various sophisticated applications. In this paper, a new technique to retrieve the encrypted and scrambled image based on discrete fractional transforms has been proposed. Two-dimensional image was encrypted using discrete fractional transforms with three fractional orders and two random phase masks placed in the two intermediate planes. The significant feature of discrete fractional transforms benefits from its extra degree of freedom that is provided by its fractional orders. Security strength was enhanced (1024!)4 times by scrambling the encrypted image. In decryption process, image retrieval is sensitive for both correct fractional order keys and scrambling algorithm. The proposed approach make the brute force attack infeasible. Mean square error and relative error are the recital parameters to verify validity of proposed method.

Modeling multiple visual words assignment for bag-of-features based medical image retrieval

KAUST Repository

Wang, Jim Jing-Yan

2012-01-01

In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments\\' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

Modeling multiple visual words assignment for bag-of-features based medical image retrieval

KAUST Repository

Wang, Jim Jing-Yan; Almasri, Islam

2012-01-01

In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

Retrieval of leaf water content spanning the visible to thermal infrared spectra

CSIR Research Space (South Africa)

Ullah, S

2014-05-01

Full Text Available ; Hunt and Rock 1989; Sepulcre-Cantó et al. 2006). 45 Retrieving leaf water content using remote sensing data, has been widely investigated in the 46 visible near infrared (VNIR) and shortwave infrared (SWIR) spectra (Thomas et al. 1971; 47 Danson et..., USA: NASA / GSFC 400 Savitzky, A., & Golay, M.J.E. (1964). Smoothing and differentiation of data by simplified Least 401 squares procedures. Analytical Chemistry, 36, 1627-1639 402 Sepulcre-Cantó, G., Zarco-Tejada, P.J., Jiménez-Muñoz, J.C., Sobrino...

Parameter retrieval of chiral metamaterials based on the state-space approach.

Science.gov (United States)

Zarifi, Davoud; Soleimani, Mohammad; Abdolali, Ali

2013-08-01

This paper deals with the introduction of an approach for the electromagnetic characterization of homogeneous chiral layers. The proposed method is based on the state-space approach and properties of a 4×4 state transition matrix. Based on this, first, the forward problem analysis through the state-space method is reviewed and properties of the state transition matrix of a chiral layer are presented and proved as two theorems. The formulation of a proposed electromagnetic characterization method is then presented. In this method, scattering data for a linearly polarized plane wave incident normally on a homogeneous chiral slab are combined with properties of a state transition matrix and provide a powerful characterization method. The main difference with respect to other well-established retrieval procedures based on the use of the scattering parameters relies on the direct computation of the transfer matrix of the slab as opposed to the conventional calculation of the propagation constant and impedance of the modes supported by the medium. The proposed approach allows avoiding nonlinearity of the problem but requires getting enough equations to fulfill the task which was provided by considering some properties of the state transition matrix. To demonstrate the applicability and validity of the method, the constitutive parameters of two well-known dispersive chiral metamaterial structures at microwave frequencies are retrieved. The results show that the proposed method is robust and reliable.

Application of MPEG-7 descriptors for content-based indexing of sports videos

Science.gov (United States)

Hoeynck, Michael; Auweiler, Thorsten; Ohm, Jens-Rainer

2003-06-01

The amount of multimedia data available worldwide is increasing every day. There is a vital need to annotate multimedia data in order to allow universal content access and to provide content-based search-and-retrieval functionalities. Since supervised video annotation can be time consuming, an automatic solution is appreciated. We review recent approaches to content-based indexing and annotation of videos for different kind of sports, and present our application for the automatic annotation of equestrian sports videos. Thereby, we especially concentrate on MPEG-7 based feature extraction and content description. We apply different visual descriptors for cut detection. Further, we extract the temporal positions of single obstacles on the course by analyzing MPEG-7 edge information and taking specific domain knowledge into account. Having determined single shot positions as well as the visual highlights, the information is jointly stored together with additional textual information in an MPEG-7 description scheme. Using this information, we generate content summaries which can be utilized in a user front-end in order to provide content-based access to the video stream, but further content-based queries and navigation on a video-on-demand streaming server.

Encryption of QR code and grayscale image in interference-based scheme with high quality retrieval and silhouette problem removal

Science.gov (United States)

Qin, Yi; Wang, Hongjuan; Wang, Zhipeng; Gong, Qiong; Wang, Danchen

2016-09-01

In optical interference-based encryption (IBE) scheme, the currently available methods have to employ the iterative algorithms in order to encrypt two images and retrieve cross-talk free decrypted images. In this paper, we shall show that this goal can be achieved via an analytical process if one of the two images is QR code. For decryption, the QR code is decrypted in the conventional architecture and the decryption has a noisy appearance. Nevertheless, the robustness of QR code against noise enables the accurate acquisition of its content from the noisy retrieval, as a result of which the primary QR code can be exactly regenerated. Thereafter, a novel optical architecture is proposed to recover the grayscale image by aid of the QR code. In addition, the proposal has totally eliminated the silhouette problem existing in the previous IBE schemes, and its effectiveness and feasibility have been demonstrated by numerical simulations.

An Optimal-Estimation-Based Aerosol Retrieval Algorithm Using OMI Near-UV Observations

Science.gov (United States)

Jeong, U; Kim, J.; Ahn, C.; Torres, O.; Liu, X.; Bhartia, P. K.; Spurr, R. J. D.; Haffner, D.; Chance, K.; Holben, B. N.

2016-01-01

An optimal-estimation(OE)-based aerosol retrieval algorithm using the OMI (Ozone Monitoring Instrument) near-ultraviolet observation was developed in this study. The OE-based algorithm has the merit of providing useful estimates of errors simultaneously with the inversion products. Furthermore, instead of using the traditional lookup tables for inversion, it performs online radiative transfer calculations with the VLIDORT (linearized pseudo-spherical vector discrete ordinate radiative transfer code) to eliminate interpolation errors and improve stability. The measurements and inversion products of the Distributed Regional Aerosol Gridded Observation Network campaign in northeast Asia (DRAGON NE-Asia 2012) were used to validate the retrieved aerosol optical thickness (AOT) and single scattering albedo (SSA). The retrieved AOT and SSA at 388 nm have a correlation with the Aerosol Robotic Network (AERONET) products that is comparable to or better than the correlation with the operational product during the campaign. The OEbased estimated error represented the variance of actual biases of AOT at 388 nm between the retrieval and AERONET measurements better than the operational error estimates. The forward model parameter errors were analyzed separately for both AOT and SSA retrievals. The surface reflectance at 388 nm, the imaginary part of the refractive index at 354 nm, and the number fine-mode fraction (FMF) were found to be the most important parameters affecting the retrieval accuracy of AOT, while FMF was the most important parameter for the SSA retrieval. The additional information provided with the retrievals, including the estimated error and degrees of freedom, is expected to be valuable for relevant studies. Detailed advantages of using the OE method were described and discussed in this paper.

Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback

Directory of Open Access Journals (Sweden)

Kompatsiaris Ioannis

2004-01-01

Full Text Available An image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions and endow the indexing and retrieval system with content-based functionalities. Low-level descriptors for the color, position, size, and shape of each region are subsequently extracted. These arithmetic descriptors are automatically associated with appropriate qualitative intermediate-level descriptors, which form a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword and their relations in a human-centered fashion. When querying for a specific semantic object (or objects, the intermediate-level descriptor values associated with both the semantic object and all image regions in the collection are initially compared, resulting in the rejection of most image regions as irrelevant. Following that, a relevance feedback mechanism, based on support vector machines and using the low-level descriptors, is invoked to rank the remaining potentially relevant image regions and produce the final query results. Experimental results and comparisons demonstrate, in practice, the effectiveness of our approach.

Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

Science.gov (United States)

Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin

2013-02-01

Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

A Novel Method of Case Representation and Retrieval in CBR for E-Learning

Science.gov (United States)

Khamparia, Aditya; Pandey, Babita

2017-01-01

In this paper we have discussed a novel method which has been developed for representation and retrieval of cases in case based reasoning (CBR) as a part of e-learning system which is based on various student features. In this approach we have integrated Artificial Neural Network (ANN) with Data mining (DM) and CBR. ANN is used to find the…

Photopolarimetric Retrievals of Snow Properties

Science.gov (United States)

Ottaviani, M.; van Diedenhoven, B.; Cairns, B.

2015-01-01

Polarimetric observations of snow surfaces, obtained in the 410-2264 nm range with the Research Scanning Polarimeter onboard the NASA ER-2 high-altitude aircraft, are analyzed and presented. These novel measurements are of interest to the remote sensing community because the overwhelming brightness of snow plagues aerosol and cloud retrievals based on airborne and spaceborne total reflection measurements. The spectral signatures of the polarized reflectance of snow are therefore worthwhile investigating in order to provide guidance for the adaptation of algorithms currently employed for the retrieval of aerosol properties over soil and vegetated surfaces. At the same time, the increased information content of polarimetric measurements allows for a meaningful characterization of the snow medium. In our case, the grains are modeled as hexagonal prisms of variable aspect ratios and microscale roughness, yielding retrievals of the grains' scattering asymmetry parameter, shape and size. The results agree with our previous findings based on a more limited data set, with the majority of retrievals leading to moderately rough crystals of extreme aspect ratios, for each scene corresponding to a single value of the asymmetry parameter.

A Review of Auditing Methods Applied to the Content of Controlled Biomedical Terminologies

Science.gov (United States)

Zhu, Xinxin; Fan, Jung-Wei; Baorto, David M.; Weng, Chunhua; Cimino, James J.

2012-01-01

Although controlled biomedical terminologies have been with us for centuries, it is only in the last couple of decades that close attention has been paid to the quality of these terminologies. The result of this attention has been the development of auditing methods that apply formal methods to assessing whether terminologies are complete and accurate. We have performed an extensive literature review to identify published descriptions of these methods and have created a framework for characterizing them. The framework considers manual, systematic and heuristic methods that use knowledge (within or external to the terminology) to measure quality factors of different aspects of the terminology content (terms, semantic classification, and semantic relationships). The quality factors examined included concept orientation, consistency, non-redundancy, soundness and comprehensive coverage. We reviewed 130 studies that were retrieved based on keyword search on publications in PubMed, and present our assessment of how they fit into our framework. We also identify which terminologies have been audited with the methods and provide examples to illustrate each part of the framework. PMID:19285571

Relevance of useful visual words in object retrieval

Science.gov (United States)

Qi, Siyuan; Luo, Yupin

2013-07-01

The most popular methods in object retrieval are almost based on bag-of-words(BOW) which is both effective and efficient. In this paper we present a method use the relations between words of the vocabulary to improve the retrieval performance based on the BOW framework. In basic BOW retrieval framework, only a few words of the vocabulary is useful for retrieval, which are spatial consistent in images. We introduce a method to useful select useful words and build a relevance between these words. We combine useful relevance with basic BOW framework and query expansion as well. The useful relevance is able to discover latent related words which is not exist in the query image, so that we can get a more accurate vector model for retrieval. Combined with query expansion method, the retrieval performance are better and fewer time cost.

Agricultural Library Information Retrieval Based on Improved Semantic Algorithm

OpenAIRE

Meiling , Xie

2014-01-01

International audience; To support users to quickly access information they need from the agricultural library’s vast information and to improve the low intelligence query service, a model for intelligent library information retrieval was constructed. The semantic web mode was introduced and the information retrieval framework was designed. The model structure consisted of three parts: Information data integration, user interface and information retrieval match. The key method supporting retr...

Retrieval of nitrogen dioxide stratospheric profiles from ground-based zenith-sky UV-visible observations: validation of the technique through correlative comparisons

Directory of Open Access Journals (Sweden)

F. Hendrick

2004-01-01

Full Text Available A retrieval algorithm based on the Optimal Estimation Method (OEM has been developed in order to provide vertical distributions of NO2 in the stratosphere from ground-based (GB zenith-sky UV-visible observations. It has been applied to observational data sets from the NDSC (Network for Detection of Stratospheric Change stations of Harestua (60° N, 10° E and Andøya (69° N, 16° E in Norway. The information content and retrieval errors have been analyzed following a formalism used for characterizing ozone profiles retrieved from solar infrared absorption spectra. In order to validate the technique, the retrieved NO2 vertical profiles and columns have been compared to correlative balloon and satellite observations. Such extensive validation of the profile and column retrievals was not reported in previously published work on the profiling from GB UV-visible measurements. A good agreement - generally better than 25% - has been found with the SAOZ (Système d'Analyse par Observations Zénithales and DOAS (Differential Optical Absorption Spectroscopy balloons. A similar agreement has been reached with correlative satellite data from the HALogen Occultation Experiment (HALOE and Polar Ozone and Aerosol Measurement (POAM III instruments above 25km of altitude. Below 25km, a systematic underestimation - by up to 40% in some cases - of both HALOE and POAM III profiles by our GB profile retrievals has been observed, pointing out more likely a limitation of both satellite instruments at these altitudes. We have concluded that our study strengthens our confidence in the reliability of the retrieval of vertical distribution information from GB UV-visible observations and offers new perspectives in the use of GB UV-visible network data for validation purposes.

Efficient Similarity Retrieval in Music Databases

DEFF Research Database (Denmark)

Ruxanda, Maria Magdalena; Jensen, Christian Søndergaard

2006-01-01

Audio music is increasingly becoming available in digital form, and the digital music collections of individuals continue to grow. Addressing the need for effective means of retrieving music from such collections, this paper proposes new techniques for content-based similarity search. Each music...

Compact binary hashing for music retrieval

Science.gov (United States)

Seo, Jin S.

2014-03-01

With the huge volume of music clips available for protection, browsing, and indexing, there is an increased attention to retrieve the information contents of the music archives. Music-similarity computation is an essential building block for browsing, retrieval, and indexing of digital music archives. In practice, as the number of songs available for searching and indexing is increased, so the storage cost in retrieval systems is becoming a serious problem. This paper deals with the storage problem by extending the supervector concept with the binary hashing. We utilize the similarity-preserving binary embedding in generating a hash code from the supervector of each music clip. Especially we compare the performance of the various binary hashing methods for music retrieval tasks on the widely-used genre dataset and the in-house singer dataset. Through the evaluation, we find an effective way of generating hash codes for music similarity estimation which improves the retrieval performance.

First comparison of formaldehyde integral contents in ABL retrieved during clear-sky and overcast conditions by ZDOAS technique

Science.gov (United States)

Ivanov, Victor; Borovski, Alexander; Postylyakov, Oleg

2017-10-01

Formaldehyde (HCHO) is involved in a lot of chemical reactions in the atmosphere. Taking into account that HCHO basically undergo by photolysis and reaction with hydroxyl radical within a few hours, short-lived VOCs and direct HCHO emissions can cause local HCHO enhancement over certain areas, and, hence, exceeding background level of HCHO can be examined as a local pollution of the atmosphere by VOCs or existence of a local HCHO source. Several retrieval algorithms applicable for DOAS measurements in cloudless were previously developed. In previous works we proposed a new algorithm applicable for the overcast conditions. The algorithm has the typical F-coefficient error of about 10% for winter season, about 5% for summer season, and varying from 15 to 45% for transition season if the atmospheric boundary layer is below the cloud base. In this paper we briefly present our results of the HCHO vertical column retrieval measured at Zvenigorod Scientific Station (ZSS) for overcast. ZSS (55°41'49''N, 36°46'29''E) is located in Moscow region in 38 km west from Moscow. Because Western winds prevail in this region, ZSS is a background station the most part of time. But in cases of Eastern wind, the air quality at ZSS is affected by Moscow megapolis, and polluted air masses formed above Moscow can reach station in a few hours. Due to the absence of alternative overcast data of HCHO, we compare our overcast data with the HCHO vertical content, which we obtained for clear sky. We investigate similarities and differences in their statistical behavior in different air mass. The average overcast HCHO data have similar to clear-sky HCHO positive temperature trends for all wind direction. We found that the average retrieved overcast HCHO contents are systematically greater than the clear-sky retrieval data. But the difference between data retrieved for the overcast and clear-sky conditions are different for Eastern and Western winds. This difference is about 0.5×1016 mol cm-2

Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

Science.gov (United States)

Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

2016-06-14

In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

A novel video recommendation system based on efficient retrieval of human actions

Science.gov (United States)

Ramezani, Mohsen; Yaghmaee, Farzin

2016-09-01

In recent years, fast growth of online video sharing eventuated new issues such as helping users to find their requirements in an efficient way. Hence, Recommender Systems (RSs) are used to find the users' most favorite items. Finding these items relies on items or users similarities. Though, many factors like sparsity and cold start user impress the recommendation quality. In some systems, attached tags are used for searching items (e.g. videos) as personalized recommendation. Different views, incomplete and inaccurate tags etc. can weaken the performance of these systems. Considering the advancement of computer vision techniques can help improving RSs. To this end, content based search can be used for finding items (here, videos are considered). In such systems, a video is taken from the user to find and recommend a list of most similar videos to the query one. Due to relating most videos to humans, we present a novel low complex scalable method to recommend videos based on the model of included action. This method has recourse to human action retrieval approaches. For modeling human actions, some interest points are extracted from each action and their motion information are used to compute the action representation. Moreover, a fuzzy dissimilarity measure is presented to compare videos for ranking them. The experimental results on HMDB, UCFYT, UCF sport and KTH datasets illustrated that, in most cases, the proposed method can reach better results than most used methods.

The effect of cue content on retrieval from autobiographical memory.

Science.gov (United States)

Uzer, Tugba; Brown, Norman R

2017-01-01

It has long been argued that personal memories are usually generated in an effortful search process in word-cueing studies. However, recent research (Uzer, Lee, & Brown, 2012) shows that direct retrieval of autobiographical memories, in response to word cues, is common. This invites the question of whether direct retrieval phenomenon is generalizable beyond the standard laboratory paradigm. Here we investigated prevalence of direct retrieval of autobiographical memories cued by specific and individuated cues versus generic cues. In Experiment 1, participants retrieved memories in response to cues from their own life (e.g., the names of friends) and generic words (e.g., chair). In Experiment 2, participants provided their personal cues two or three months prior to coming to the lab (min: 75days; max: 100days). In each experiment, RT was measured and participants reported whether memories were directly retrieved or generated on each trial. Results showed that personal cues elicited a high rate of direct retrieval. Personal cues were more likely to elicit direct retrieval than generic word cues, and as a consequence, participants responded faster, on average, to the former than to the latter. These results challenge the constructive view of autobiographical memory and suggest that autobiographical memories consist of pre-stored event representations, which are largely governed by associative mechanisms. These demonstrations offer theoretically interesting questions such as why are we not overwhelmed with directly retrieved memories cued by everyday familiar surroundings? Copyright © 2016 Elsevier B.V. All rights reserved.

Extension of the Hapke bidirectional reflectance model to retrieve soil water content

Directory of Open Access Journals (Sweden)

G.-J. Yang

2011-07-01

Full Text Available Soil moisture links the hydrologic cycle and the energy budget of land surfaces by regulating latent heat fluxes. An accurate assessment of the spatial and temporal variation of soil moisture is important to the study of surface biogeophysical processes. Although remote sensing has proven to be one of the most powerful tools for obtaining land surface parameters, no effective methodology yet exists for in situ soil moisture measurement based on a Bidirectional Reflectance Distribution Function (BRDF model, such as the Hapke model. To retrieve and analyze soil moisture, this study applied the soil water parametric (SWAP-Hapke model, which introduced the equivalent water thickness of soil, to ground multi-angular and hyperspectral observations coupled with, Powell-Ant Colony Algorithm methods. The inverted soil moisture data resulting from our method coincided with in situ measurements (R² = 0.867, RMSE = 0.813 based on three selected bands (672 nm, 866 nm, 2209 nm. It proved that the extended Hapke model can be used to estimate soil moisture with high accuracy based on the field multi-angle and multispectral remote sensing data.

Retrieval with Clustering in a Case-Based Reasoning System for Radiotherapy Treatment Planning

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Khussainova, Gulmira; Petrovic, Sanja; Jagannathan, Rupa

2015-05-01

Radiotherapy treatment planning aims to deliver a sufficient radiation dose to cancerous tumour cells while sparing healthy organs in the tumour surrounding area. This is a trial and error process highly dependent on the medical staff's experience and knowledge. Case-Based Reasoning (CBR) is an artificial intelligence tool that uses past experiences to solve new problems. A CBR system has been developed to facilitate radiotherapy treatment planning for brain cancer. Given a new patient case the existing CBR system retrieves a similar case from an archive of successfully treated patient cases with the suggested treatment plan. The next step requires adaptation of the retrieved treatment plan to meet the specific demands of the new case. The CBR system was tested by medical physicists for the new patient cases. It was discovered that some of the retrieved cases were not suitable and could not be adapted for the new cases. This motivated us to revise the retrieval mechanism of the existing CBR system by adding a clustering stage that clusters cases based on their tumour positions. A number of well-known clustering methods were investigated and employed in the retrieval mechanism. Results using real world brain cancer patient cases have shown that the success rate of the new CBR retrieval is higher than that of the original system.

Retrieval with Clustering in a Case-Based Reasoning System for Radiotherapy Treatment Planning

International Nuclear Information System (INIS)

Khussainova, Gulmira; Petrovic, Sanja; Jagannathan, Rupa

2015-01-01

Radiotherapy treatment planning aims to deliver a sufficient radiation dose to cancerous tumour cells while sparing healthy organs in the tumour surrounding area. This is a trial and error process highly dependent on the medical staff's experience and knowledge. Case-Based Reasoning (CBR) is an artificial intelligence tool that uses past experiences to solve new problems. A CBR system has been developed to facilitate radiotherapy treatment planning for brain cancer. Given a new patient case the existing CBR system retrieves a similar case from an archive of successfully treated patient cases with the suggested treatment plan. The next step requires adaptation of the retrieved treatment plan to meet the specific demands of the new case. The CBR system was tested by medical physicists for the new patient cases. It was discovered that some of the retrieved cases were not suitable and could not be adapted for the new cases. This motivated us to revise the retrieval mechanism of the existing CBR system by adding a clustering stage that clusters cases based on their tumour positions. A number of well-known clustering methods were investigated and employed in the retrieval mechanism. Results using real world brain cancer patient cases have shown that the success rate of the new CBR retrieval is higher than that of the original system. (paper)

Video content analysis of surgical procedures.

Science.gov (United States)

Loukas, Constantinos

2018-02-01

In addition to its therapeutic benefits, minimally invasive surgery offers the potential for video recording of the operation. The videos may be archived and used later for reasons such as cognitive training, skills assessment, and workflow analysis. Methods from the major field of video content analysis and representation are increasingly applied in the surgical domain. In this paper, we review recent developments and analyze future directions in the field of content-based video analysis of surgical operations. The review was obtained from PubMed and Google Scholar search on combinations of the following keywords: 'surgery', 'video', 'phase', 'task', 'skills', 'event', 'shot', 'analysis', 'retrieval', 'detection', 'classification', and 'recognition'. The collected articles were categorized and reviewed based on the technical goal sought, type of surgery performed, and structure of the operation. A total of 81 articles were included. The publication activity is constantly increasing; more than 50% of these articles were published in the last 3 years. Significant research has been performed for video task detection and retrieval in eye surgery. In endoscopic surgery, the research activity is more diverse: gesture/task classification, skills assessment, tool type recognition, shot/event detection and retrieval. Recent works employ deep neural networks for phase and tool recognition as well as shot detection. Content-based video analysis of surgical operations is a rapidly expanding field. Several future prospects for research exist including, inter alia, shot boundary detection, keyframe extraction, video summarization, pattern discovery, and video annotation. The development of publicly available benchmark datasets to evaluate and compare task-specific algorithms is essential.

Chiral metamaterials characterisation using the wave propagation retrieval method

DEFF Research Database (Denmark)

Andryieuski, Andrei; Lavrinenko, Andrei; Malureanu, Radu

2010-01-01

In this presentation we extend the wave propagation method for the retrieval of the effective properties to the case of chiral metamaterials with circularly polarised eigenwaves. The method is unambiguous, simple and provides bulk effective parameters. Advantages and constraints are discussed...

Generating Concise Rules for Human Motion Retrieval

Science.gov (United States)

Mukai, Tomohiko; Wakisaka, Ken-Ichi; Kuriyama, Shigeru

This paper proposes a method for retrieving human motion data with concise retrieval rules based on the spatio-temporal features of motion appearance. Our method first converts motion clip into a form of clausal language that represents geometrical relations between body parts and their temporal relationship. A retrieval rule is then learned from the set of manually classified examples using inductive logic programming (ILP). ILP automatically discovers the essential rule in the same clausal form with a user-defined hypothesis-testing procedure. All motions are indexed using this clausal language, and the desired clips are retrieved by subsequence matching using the rule. Such rule-based retrieval offers reasonable performance and the rule can be intuitively edited in the same language form. Consequently, our method enables efficient and flexible search from a large dataset with simple query language.

Music information retrieval based on tonal harmony

NARCIS (Netherlands)

de Haas, W.B.|info:eu-repo/dai/nl/304841250

2012-01-01

With the emergence of large scale digitalisation of music, content-based methods to maintain, structure, and provide access to digital music repositories have become increasingly important. This doctoral dissertation covers a wide range of methods that aim to aid in the organisation of music

A Novel Relevance Feedback Approach Based on Similarity Measure Modification in an X-Ray Image Retrieval System Based on Fuzzy Representation Using Fuzzy Attributed Relational Graph

Directory of Open Access Journals (Sweden)

Hossien Pourghassem

2011-04-01

Full Text Available Relevance feedback approaches is used to improve the performance of content-based image retrieval systems. In this paper, a novel relevance feedback approach based on similarity measure modification in an X-ray image retrieval system based on fuzzy representation using fuzzy attributed relational graph (FARG is presented. In this approach, optimum weight of each feature in feature vector is calculated using similarity rate between query image and relevant and irrelevant images in user feedback. The calculated weight is used to tune fuzzy graph matching algorithm as a modifier parameter in similarity measure. The standard deviation of the retrieved image features is applied to calculate the optimum weight. The proposed image retrieval system uses a FARG for representation of images, a fuzzy matching graph algorithm as similarity measure and a semantic classifier based on merging scheme for determination of the search space in image database. To evaluate relevance feedback approach in the proposed system, a standard X-ray image database consisting of 10000 images in 57 classes is used. The improvement of the evaluation parameters shows proficiency and efficiency of the proposed system.

Spline based iterative phase retrieval algorithm for X-ray differential phase contrast radiography.

Science.gov (United States)

Nilchian, Masih; Wang, Zhentian; Thuering, Thomas; Unser, Michael; Stampanoni, Marco

2015-04-20

Differential phase contrast imaging using grating interferometer is a promising alternative to conventional X-ray radiographic methods. It provides the absorption, differential phase and scattering information of the underlying sample simultaneously. Phase retrieval from the differential phase signal is an essential problem for quantitative analysis in medical imaging. In this paper, we formalize the phase retrieval as a regularized inverse problem, and propose a novel discretization scheme for the derivative operator based on B-spline calculus. The inverse problem is then solved by a constrained regularized weighted-norm algorithm (CRWN) which adopts the properties of B-spline and ensures a fast implementation. The method is evaluated with a tomographic dataset and differential phase contrast mammography data. We demonstrate that the proposed method is able to produce phase image with enhanced and higher soft tissue contrast compared to conventional absorption-based approach, which can potentially provide useful information to mammographic investigations.

The Electronic Data and Retrieval of the Secret History of the Mongols

Directory of Open Access Journals (Sweden)

Di Jiang

2007-07-01

Full Text Available This paper discusses the principle of electronic data and retrieval methods for the Secret History of the Mongols, which is a great classical historical work written in the 13th century with Chinese characters transliterated from Mongol. This handwritten work contains rather rich text information, which should be the contents of forming an electronic database. There are in the original book multi-types of information, including layouts, volumes, chapters, characters, interlinear translation, segments, and Chinese translation, each format of which has been approached in detail and divided separately with markers. On the basis of analysis, our project builds up a complete electronic retrieval system for this great book, which resolves the return to the original shape of the archaic handwriting form with three lines representing one content. The sorting methods of the system are also designed according to the original text formats, namely concordance technology, which can print out retrieved objects with their contexts, retrieve with statistical data, and freely browse search.

Deep Correlated Holistic Metric Learning for Sketch-Based 3D Shape Retrieval.

Science.gov (United States)

Dai, Guoxian; Xie, Jin; Fang, Yi

2018-07-01

How to effectively retrieve desired 3D models with simple queries is a long-standing problem in computer vision community. The model-based approach is quite straightforward but nontrivial, since people could not always have the desired 3D query model available by side. Recently, large amounts of wide-screen electronic devices are prevail in our daily lives, which makes the sketch-based 3D shape retrieval a promising candidate due to its simpleness and efficiency. The main challenge of sketch-based approach is the huge modality gap between sketch and 3D shape. In this paper, we proposed a novel deep correlated holistic metric learning (DCHML) method to mitigate the discrepancy between sketch and 3D shape domains. The proposed DCHML trains two distinct deep neural networks (one for each domain) jointly, which learns two deep nonlinear transformations to map features from both domains into a new feature space. The proposed loss, including discriminative loss and correlation loss, aims to increase the discrimination of features within each domain as well as the correlation between different domains. In the new feature space, the discriminative loss minimizes the intra-class distance of the deep transformed features and maximizes the inter-class distance of the deep transformed features to a large margin within each domain, while the correlation loss focused on mitigating the distribution discrepancy across different domains. Different from existing deep metric learning methods only with loss at the output layer, our proposed DCHML is trained with loss at both hidden layer and output layer to further improve the performance by encouraging features in the hidden layer also with desired properties. Our proposed method is evaluated on three benchmarks, including 3D Shape Retrieval Contest 2013, 2014, and 2016 benchmarks, and the experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.

Learning Object Retrieval and Aggregation Based on Learning Styles

Science.gov (United States)

Ramirez-Arellano, Aldo; Bory-Reyes, Juan; Hernández-Simón, Luis Manuel

2017-01-01

The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students' learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step…

A Process Model for Goal-Based Information Retrieval

Directory of Open Access Journals (Sweden)

Harvey Hyman

2014-12-01

Full Text Available In this paper we examine the domain of information search and propose a "goal-based" approach to study search strategy. We describe "goal-based information search" using a framework of Knowledge Discovery. We identify two Information Retrieval (IR goals using the constructs of Knowledge Acquisition (KA and Knowledge Explanation (KE. We classify these constructs into two specific information problems: An exploration-exploitation problem and an implicit-explicit problem. Our proposed framework is an extension of prior work in this domain, applying an IR Process Model originally developed for Legal-IR and adapted to Medical-IR. The approach in this paper is guided by the recent ACM-SIG Medical Information Retrieval (MedIR Workshop definition: "methodologies and technologies that seek to improve access to medical information archives via a process of information retrieval."

[Simulation of vegetation indices optimizing under retrieval of vegetation biochemical parameters based on PROSPECT + SAIL model].

Science.gov (United States)

Wu, Ling; Liu, Xiang-Nan; Zhou, Bo-Tian; Liu, Chuan-Hao; Li, Lu-Feng

2012-12-01

This study analyzed the sensitivities of three vegetation biochemical parameters [chlorophyll content (Cab), leaf water content (Cw), and leaf area index (LAI)] to the changes of canopy reflectance, with the effects of each parameter on the wavelength regions of canopy reflectance considered, and selected three vegetation indices as the optimization comparison targets of cost function. Then, the Cab, Cw, and LAI were estimated, based on the particle swarm optimization algorithm and PROSPECT + SAIL model. The results showed that retrieval efficiency with vegetation indices as the optimization comparison targets of cost function was better than that with all spectral reflectance. The correlation coefficients (R2) between the measured and estimated values of Cab, Cw, and LAI were 90.8%, 95.7%, and 99.7%, and the root mean square errors of Cab, Cw, and LAI were 4.73 microg x cm(-2), 0.001 g x cm(-2), and 0.08, respectively. It was suggested that to adopt vegetation indices as the optimization comparison targets of cost function could effectively improve the efficiency and precision of the retrieval of biochemical parameters based on PROSPECT + SAIL model.

A retrieval-based approach to eliminating hindsight bias.

Science.gov (United States)

Van Boekel, Martin; Varma, Keisha; Varma, Sashank

2017-03-01

Individuals exhibit hindsight bias when they are unable to recall their original responses to novel questions after correct answers are provided to them. Prior studies have eliminated hindsight bias by modifying the conditions under which original judgments or correct answers are encoded. Here, we explored whether hindsight bias can be eliminated by manipulating the conditions that hold at retrieval. Our retrieval-based approach predicts that if the conditions at retrieval enable sufficient discrimination of memory representations of original judgments from memory representations of correct answers, then hindsight bias will be reduced or eliminated. Experiment 1 used the standard memory design to replicate the hindsight bias effect in middle-school students. Experiments 2 and 3 modified the retrieval phase of this design, instructing participants beforehand that they would be recalling both their original judgments and the correct answers. As predicted, this enabled participants to form compound retrieval cues that discriminated original judgment traces from correct answer traces, and eliminated hindsight bias. Experiment 4 found that when participants were not instructed beforehand that they would be making both recalls, they did not form discriminating retrieval cues, and hindsight bias returned. These experiments delineate the retrieval conditions that produce-and fail to produce-hindsight bias.

Retrieval-Based Learning: Positive Effects of Retrieval Practice in Elementary School Children

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Jeffrey D. Karpicke

2016-03-01

Full Text Available A wealth of research has demonstrated that practicing retrieval is a powerful way to enhance learning. However, nearly all prior research has examined retrieval practice with college students. Little is known about retrieval practice in children, and even less is known about possible individual differences in retrieval practice. In three experiments, 88 children (mean age 10 years studied a list of words and either restudied the items or practiced retrieving them. They then took a final free recall test (Experiments 1 and 2 or recognition test (Experiment 3. In all experiments, children showed robust retrieval practice effects. Although a range of individual differences in reading comprehension and processing speed were observed among these children, the benefits of retrieval practice were independent of these factors. The results contribute to the growing body of research supporting the mnemonic benefits of retrieval practice and provide preliminary evidence that practicing retrieval may be an effective learning strategy for children with varying levels of reading comprehension and processing speed.

Statistical retrieval of thin liquid cloud microphysical properties using ground-based infrared and microwave observations

Science.gov (United States)

Marke, Tobias; Ebell, Kerstin; Löhnert, Ulrich; Turner, David D.

2016-12-01

In this article, liquid water cloud microphysical properties are retrieved by a combination of microwave and infrared ground-based observations. Clouds containing liquid water are frequently occurring in most climate regimes and play a significant role in terms of interaction with radiation. Small perturbations in the amount of liquid water contained in the cloud can cause large variations in the radiative fluxes. This effect is enhanced for thin clouds (liquid water path, LWP cloud properties crucial. Due to large relative errors in retrieving low LWP values from observations in the microwave domain and a high sensitivity for infrared methods when the LWP is low, a synergistic retrieval based on a neural network approach is built to estimate both LWP and cloud effective radius (reff). These statistical retrievals can be applied without high computational demand but imply constraints like prior information on cloud phase and cloud layering. The neural network retrievals are able to retrieve LWP and reff for thin clouds with a mean relative error of 9% and 17%, respectively. This is demonstrated using synthetic observations of a microwave radiometer (MWR) and a spectrally highly resolved infrared interferometer. The accuracy and robustness of the synergistic retrievals is confirmed by a low bias in a radiative closure study for the downwelling shortwave flux, even for marginally invalid scenes. Also, broadband infrared radiance observations, in combination with the MWR, have the potential to retrieve LWP with a higher accuracy than a MWR-only retrieval.

Fractional Fourier domain optical image hiding using phase retrieval algorithm based on iterative nonlinear double random phase encoding.

Science.gov (United States)

Wang, Xiaogang; Chen, Wen; Chen, Xudong

2014-09-22

We present a novel image hiding method based on phase retrieval algorithm under the framework of nonlinear double random phase encoding in fractional Fourier domain. Two phase-only masks (POMs) are efficiently determined by using the phase retrieval algorithm, in which two cascaded phase-truncated fractional Fourier transforms (FrFTs) are involved. No undesired information disclosure, post-processing of the POMs or digital inverse computation appears in our proposed method. In order to achieve the reduction in key transmission, a modified image hiding method based on the modified phase retrieval algorithm and logistic map is further proposed in this paper, in which the fractional orders and the parameters with respect to the logistic map are regarded as encryption keys. Numerical results have demonstrated the feasibility and effectiveness of the proposed algorithms.

A novel method for efficient archiving and retrieval of biomedical images using MPEG-7

Science.gov (United States)

Meyer, Joerg; Pahwa, Ash

2004-10-01

Digital archiving and efficient retrieval of radiological scans have become critical steps in contemporary medical diagnostics. Since more and more images and image sequences (single scans or video) from various modalities (CT/MRI/PET/digital X-ray) are now available in digital formats (e.g., DICOM-3), hospitals and radiology clinics need to implement efficient protocols capable of managing the enormous amounts of data generated daily in a typical clinical routine. We present a method that appears to be a viable way to eliminate the tedious step of manually annotating image and video material for database indexing. MPEG-7 is a new framework that standardizes the way images are characterized in terms of color, shape, and other abstract, content-related criteria. A set of standardized descriptors that are automatically generated from an image is used to compare an image to other images in a database, and to compute the distance between two images for a given application domain. Text-based database queries can be replaced with image-based queries using MPEG-7. Consequently, image queries can be conducted without any prior knowledge of the keys that were used as indices in the database. Since the decoding and matching steps are not part of the MPEG-7 standard, this method also enables searches that were not planned by the time the keys were generated.

Combining textual and visual information for image retrieval in the medical domain.

Science.gov (United States)

Gkoufas, Yiannis; Morou, Anna; Kalamboukis, Theodore

2011-01-01

In this article we have assembled the experience obtained from our participation in the imageCLEF evaluation task over the past two years. Exploitation on the use of linear combinations for image retrieval has been attempted by combining visual and textual sources of images. From our experiments we conclude that a mixed retrieval technique that applies both textual and visual retrieval in an interchangeably repeated manner improves the performance while overcoming the scalability limitations of visual retrieval. In particular, the mean average precision (MAP) has increased from 0.01 to 0.15 and 0.087 for 2009 and 2010 data, respectively, when content-based image retrieval (CBIR) is performed on the top 1000 results from textual retrieval based on natural language processing (NLP).

Retrieving top-k prestige-based relevant spatial web objects

DEFF Research Database (Denmark)

Cao, Xin; Cong, Gao; Jensen, Christian S.

2010-01-01

The location-aware keyword query returns ranked objects that are near a query location and that have textual descriptions that match query keywords. This query occurs inherently in many types of mobile and traditional web services and applications, e.g., Yellow Pages and Maps services. Previous...... of prestige-based relevance to capture both the textual relevance of an object to a query and the effects of nearby objects. Based on this, a new type of query, the Location-aware top-k Prestige-based Text retrieval (LkPT) query, is proposed that retrieves the top-k spatial web objects ranked according...... to both prestige-based relevance and location proximity. We propose two algorithms that compute LkPT queries. Empirical studies with real-world spatial data demonstrate that LkPT queries are more effective in retrieving web objects than a previous approach that does not consider the effects of nearby...

The retrieval of profile and chemical information from ground-based UV-visible spectroscopic measurements

International Nuclear Information System (INIS)

Schofield, R.; Connor, B.J.; Kreher, K.; Johnston, P.V.; Rodgers, C.D.

2004-01-01

An algorithm has been developed to retrieve altitude information at different diurnal stages for trace gas species by combining direct-sun and zenith-sky UV-visible differential slant column density (DSCD) measurements. DSCDs are derived here using differential optical absorption spectroscopy. Combining the complementary zenith-sky measurements (sensitive to the stratosphere) with direct-sun measurements (sensitive to the troposphere) allows this vertical distinction. Trace gas species such as BrO and NO 2 have vertical profiles with strong diurnal dependence. Information about the diurnal variation is simultaneously retrieved with the altitude distribution of the trace gas. The retrieval is a formal optimal estimation profile retrieval, allowing a complete assessment of information content and errors

Implementation of Texture Based Image Retrieval Using M-band Wavelet Transform

Institute of Scientific and Technical Information of China (English)

LiaoYa-li; Yangyan; CaoYang

2003-01-01

Wavelet transform has attracted attention because it is a very useful tool for signal analyzing. As a fundamental characteristic of an image, texture traits play an important role in the human vision system for recognition and interpretation of images. The paper presents an approach to implement texture-based image retrieval using M-band wavelet transform. Firstly the traditional 2-band wavelet is extended to M-band wavelet transform. Then the wavelet moments are computed by M-band wavelet coefficients in the wavelet domain. The set of wavelet moments forms the feature vector related to the texture distribution of each wavelet images. The distances between the feature vectors describe the similarities of different images. The experimental result shows that the M-band wavelet moment features of the images are effective for image indexing.The retrieval method has lower computational complexity, yet it is capable of giving better retrieval performance for a given medical image database.

Dead fuel moisture estimation with MSG-SEVIRI data. Retrieval of meteorological data for the calculation of the equilibrium moisture content

DEFF Research Database (Denmark)

Nieto Solana, Hector; Sandholt, Inge; Aguado, Inmaculada

2010-01-01

In this study we propose to use remote sensing data to estimate hourly meteorological data and then assess the moisture content of dead fuels. Three different models to estimate the equilibrium moisture content (EMC) were applied together with remotely sensed retrieved air temperature and relative...... humidity. The input data were acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, on board the Meteosat Second Generation (MSG) satellite, from which air temperature and relative humidity were estimated every 15 min. Air temperature estimations are based on the Temperature-Vegetation...... Index (TVX) algorithm. This algorithm exploits the inverse linear relationship between the land surface temperature and the vegetation fractional cover. This relationship was evaluated in a spatial window where the meteorological forcing is assumed to be constant. To estimate the vapour pressure...

GRAMMAR RULE BASED INFORMATION RETRIEVAL MODEL FOR BIG DATA

Directory of Open Access Journals (Sweden)

T. Nadana Ravishankar

2015-07-01

Full Text Available Though Information Retrieval (IR in big data has been an active field of research for past few years; the popularity of the native languages presents a unique challenge in big data information retrieval systems. There is a need to retrieve information which is present in English and display it in the native language for users. This aim of cross language information retrieval is complicated by unique features of the native languages such as: morphology, compound word formations, word spelling variations, ambiguity, word synonym, other language influence and etc. To overcome some of these issues, the native language is modeled using a grammar rule based approach in this work. The advantage of this approach is that the native language is modeled and its unique features are encoded using a set of inference rules. This rule base coupled with the customized ontological system shows considerable potential and is found to show better precision and recall.

Impact of Metadata on Full-text Information Retrieval Performance: An Experimental Research on a Small Scale Turkish Corpus

Directory of Open Access Journals (Sweden)

Çağdaş Çapkın

2016-12-01

Full Text Available Information institutions use text-based information retrieval systems to store, index and retrieve metadata, full-text, or both metadata and full-text (hybrid contents. The aim of this research was to evaluate impact of these contents on information retrieval performance. For this purpose, metadata (MIR, full-text (FIR and hybrid (HIR content information retrieval systems were developed with default Lucene information retrieval model for a small scale Turkish corpus. In order to evaluate performance of this three systems, “precision - recall” and “normalized recall” tests were conducted. Experimental findings showed that there were no significant differences between MIR and FIR in mean average precision (MAP performance. On the other hand, MAP performance of HIR was significantly higher in comparison to MIR and FIR. When information retrieval performance was evaluated as user-centered, the “normalized recall” performances of MIR and HIR were significantly higher than FIR. Additionally, there were no significant differences between the systems in retrieved relevant document means. Processing different types of contents such as metadata and full-text had some advantages and disadvantages for information retrieval systems in terms of term management. The advantages brought together in hybrid content processing (HIR and information retrieval performance improved.

Structure retrieval in HREM

International Nuclear Information System (INIS)

Gribelyuk, M.A.

1991-01-01

A new iteration method for direct structure retrieval starting from the exit plane-wave function Ψ e (r) is proposed and tested on models. The imaginary part of the potential cannot be retrieved. The effects of the limited resolution of Ψ e (r) as well as neglect of high-order Laue-zone effects and the choice of the starting potential on the result are discussed. The procedure is found to be preferable to that based on the subsequent approximation method with respect to a higher convergence rate. It is shown that an error as low as 10% may be obtained for the real part of the retrieved potential up to vertical strokeσV(r)tvertical stroke<5. (orig.)

Attention-based image similarity measure with application to content-based information retrieval

Science.gov (United States)

Stentiford, Fred W. M.

2003-01-01

Whilst storage and capture technologies are able to cope with huge numbers of images, image retrieval is in danger of rendering many repositories valueless because of the difficulty of access. This paper proposes a similarity measure that imposes only very weak assumptions on the nature of the features used in the recognition process. This approach does not make use of a pre-defined set of feature measurements which are extracted from a query image and used to match those from database images, but instead generates features on a trial and error basis during the calculation of the similarity measure. This has the significant advantage that features that determine similarity can match whatever image property is important in a particular region whether it be a shape, a texture, a colour or a combination of all three. It means that effort is expended searching for the best feature for the region rather than expecting that a fixed feature set will perform optimally over the whole area of an image and over every image in a database. The similarity measure is evaluated on a problem of distinguishing similar shapes in sets of black and white symbols.

Empirical wind retrieval model based on SAR spectrum measurements

Science.gov (United States)

Panfilova, Maria; Karaev, Vladimir; Balandina, Galina; Kanevsky, Mikhail; Portabella, Marcos; Stoffelen, Ad

ambiguity from polarimetric SAR. A criterion based on the complex correlation coefficient between the VV and VH signals sign is applied to select the wind direction. An additional quality control on the wind speed value retrieved with the spectral method is applied. Here, we use the direction obtained with the spectral method and the backscattered signal for CMOD wind speed estimate. The algorithm described above may be refined by the use of numerous SAR data and wind measurements. In the present preliminary work the first results of SAR images combined with in situ data processing are presented. Our results are compared to the results obtained using previously developed models CMOD, C-2PO for VH polarization and statistical wind retrieval approaches [1]. Acknowledgments. This work is supported by the Russian Foundation of Basic Research (grants 13-05-00852-a). [1] M. Portabella, A. Stoffelen, J. A. Johannessen, Toward an optimal inversion method for synthetic aperture radar wind retrieval, Journal of geophysical research, V. 107, N C8, 2002

Ontology-Based Retrieval of Spatially Related Objects for Location Based Services

Science.gov (United States)

Haav, Hele-Mai; Kaljuvee, Aivi; Luts, Martin; Vajakas, Toivo

Advanced Location Based Service (LBS) applications have to integrate information stored in GIS, information about users' preferences (profile) as well as contextual information and information about application itself. Ontology engineering provides methods to semantically integrate several data sources. We propose an ontology-driven LBS development framework: the paper describes the architecture of ontologies and their usage for retrieval of spatially related objects relevant to the user. Our main contribution is to enable personalised ontology driven LBS by providing a novel approach for defining personalised semantic spatial relationships by means of ontologies. The approach is illustrated by an industrial case study.

Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar

Directory of Open Access Journals (Sweden)

Hao Tang

2016-09-01

Full Text Available Airborne single-photon lidar (SPL is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidar instruments. However, SPL instruments use green wavelength (532 nm lasers, which are sensitive to background solar noise, and therefore SPL point clouds require more elaborate noise filtering than other lidar instruments to determine canopy heights, particularly in daytime acquisitions. Histogram-based aggregation is a commonly used approach for removing noise from photon counting lidar data, but it reduces the resolution of the dataset. Here we present an alternate voxel-based spatial filtering method that filters noise points efficiently while largely preserving the spatial integrity of SPL data. We develop and test our algorithms on an experimental SPL dataset acquired over Garrett County in Maryland, USA. We then compare canopy attributes retrieved using our new algorithm with those obtained from the conventional histogram binning approach. Our results show that canopy heights derived using the new algorithm have a strong agreement with field-measured heights (r2 = 0.69, bias = 0.42 m, RMSE = 4.85 m and discrete return lidar heights (r2 = 0.94, bias = 1.07 m, RMSE = 2.42 m. Results are consistently better than height accuracies from the histogram method (field data: r2 = 0.59, bias = 0.00 m, RMSE = 6.25 m; DRL: r2 = 0.78, bias = −0.06 m and RMSE = 4.88 m. Furthermore, we find that the spatial-filtering method retains fine-scale canopy structure detail and has lower errors over steep slopes. We therefore believe that automated spatial filtering algorithms such as the one presented here can support large-scale, canopy structure mapping from airborne SPL data.

Tropospheric nitrogen dioxide column retrieval based on ground-based zenith-sky DOAS observations

Science.gov (United States)

Tack, F. M.; Hendrick, F.; Pinardi, G.; Fayt, C.; Van Roozendael, M.

2013-12-01

A retrieval approach has been developed to derive tropospheric NO2 vertical column amounts from ground-based zenith-sky measurements of scattered sunlight. Zenith radiance spectra are observed in the visible range by the BIRA-IASB Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument and analyzed by the DOAS technique, based on a least-squares spectral fitting. In recent years, this technique has shown to be a well-suited remote sensing tool for monitoring atmospheric trace gases. The retrieval algorithm is developed and validated based on a two month dataset acquired from June to July 2009 in the framework of the Cabauw (51.97° N, 4.93° E) Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI). Once fully operational, the retrieval approach can be applied to observations from stations of the Network for the Detection of Atmospheric Composition Change (NDACC). The obtained tropospheric vertical column amounts are compared with the multi-axis retrieval from the BIRA-IASB MAX-DOAS instrument and the retrieval from a zenith-viewing only SAOZ instrument (Système d'Analyse par Observations Zénithales), owned by Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS). First results show a good agreement for the whole time series with the multi-axis retrieval (R = 0.82; y = 0.88x + 0.30) as well as with the SAOZ retrieval (R = 0.85; y = 0.76x + 0.28 ). Main error sources arise from the uncertainties in the determination of tropospheric and stratospheric air mass factors, the stratospheric NO2 abundances and the residual amount in the reference spectrum. However zenith-sky measurements have been commonly used over the last decades for stratospheric monitoring, this study also illustrates the suitability for retrieval of tropospheric column amounts. As there are long time series of zenith-sky acquisitions available, the developed approach offers new perspectives with regard to the use of observations from the NDACC

Unsupervised Segmentation Methods of TV Contents

Directory of Open Access Journals (Sweden)

Elie El-Khoury

2010-01-01

Full Text Available We present a generic algorithm to address various temporal segmentation topics of audiovisual contents such as speaker diarization, shot, or program segmentation. Based on a GLR approach, involving the ΔBIC criterion, this algorithm requires the value of only a few parameters to produce segmentation results at a desired scale and on most typical low-level features used in the field of content-based indexing. Results obtained on various corpora are of the same quality level than the ones obtained by other dedicated and state-of-the-art methods.

Four Methods for LIDAR Retrieval of Microscale Wind Fields

Directory of Open Access Journals (Sweden)

Thomas Naini

2012-08-01

Full Text Available This paper evaluates four wind retrieval methods for micro-scale meteorology applications with volume and time resolution in the order of 30m³ and 5 s. Wind field vectors are estimated using sequential time-lapse volume images of aerosol density fluctuations. Suitably designed mono-static scanning backscatter LIDAR systems, which are sensitive to atmospheric density aerosol fluctuations, are expected to be ideal for this purpose. An important application is wind farm siting and evaluation. In this case, it is necessary to look at the complicated region between the earth’s surface and the boundary layer, where wind can be turbulent and fractal scaling from millimeter to kilometer. The methods are demonstrated using first a simple randomized moving hard target, and then with a physics based stochastic space-time dynamic turbulence model. In the latter case the actual vector wind field is known, allowing complete space-time error analysis. Two of the methods, the semblance method and the spatio-temporal method, are found to be most suitable for wind field estimation.

Techniques for Soundscape Retrieval and Synthesis

Science.gov (United States)

Mechtley, Brandon Michael

The study of acoustic ecology is concerned with the manner in which life interacts with its environment as mediated through sound. As such, a central focus is that of the soundscape: the acoustic environment as perceived by a listener. This dissertation examines the application of several computational tools in the realms of digital signal processing, multimedia information retrieval, and computer music synthesis to the analysis of the soundscape. Namely, these tools include a) an open source software library, Sirens, which can be used for the segmentation of long environmental field recordings into individual sonic events and compare these events in terms of acoustic content, b) a graph-based retrieval system that can use these measures of acoustic similarity and measures of semantic similarity using the lexical database WordNet to perform both text-based retrieval and automatic annotation of environmental sounds, and c) new techniques for the dynamic, realtime parametric morphing of multiple field recordings, informed by the geographic paths along which they were recorded.

Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

KAUST Repository

Houborg, Rasmus

2015-01-19

Leaf area index (LAI) and leaf chlorophyll content (Chll) represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and Chll provide critical information on vegetation density, vitality and photosynthetic potentials. However, simultaneous retrieval of LAI and Chll from space observations is extremely challenging. Regularization strategies are required to increase the robustness and accuracy of retrieved properties and enable more reliable separation of soil, leaf and canopy parameters. To address these challenges, the REGularized canopy reFLECtance model (REGFLEC) inversion system was refined to incorporate enhanced techniques for exploiting ancillary LAI and temporal information derived from multiple satellite scenes. In this current analysis, REGFLEC is applied to a time-series of Landsat data.A novel aspect of the REGFLEC approach is the fact that no site-specific data are required to calibrate the model, which may be run in a largely automated fashion using information extracted entirely from image-based and other widely available datasets. Validation results, based upon in-situ LAI and Chll observations collected over maize and soybean fields in central Nebraska for the period 2001-2005, demonstrate Chll retrieval with a relative root-mean-square-deviation (RMSD) on the order of 19% (RMSD=8.42μgcm-2). While Chll retrievals were clearly influenced by the version of the leaf optical properties model used (PROSPECT), the application of spatio-temporal regularization constraints was shown to be critical for estimating Chll with sufficient accuracy. REGFLEC also reproduced the dynamics of in-situ measured LAI well (r2 =0.85), but estimates were biased low, particularly over maize (LAI was underestimated by ~36 %). This disparity may be attributed to differences between effective and true LAI caused by significant foliage clumping not being properly accounted for in the canopy

A high accuracy land use/cover retrieval system

Directory of Open Access Journals (Sweden)

Alaa Hefnawy

2012-03-01

Full Text Available The effects of spatial resolution on the accuracy of mapping land use/cover types have received increasing attention as a large number of multi-scale earth observation data become available. Although many methods of semi automated image classification of remotely sensed data have been established for improving the accuracy of land use/cover classification during the past 40 years, most of them were employed in single-resolution image classification, which led to unsatisfactory results. In this paper, we propose a multi-resolution fast adaptive content-based retrieval system of satellite images. Through our proposed system, we apply a Super Resolution technique for the Landsat-TM images to have a high resolution dataset. The human–computer interactive system is based on modified radial basis function for retrieval of satellite database images. We apply the backpropagation supervised artificial neural network classifier for both the multi and single resolution datasets. The results show significant improved land use/cover classification accuracy for the multi-resolution approach compared with those from single-resolution approach.

Facilitating medical information search using Google Glass connected to a content-based medical image retrieval system.

Science.gov (United States)

Widmer, Antoine; Schaer, Roger; Markonis, Dimitrios; Muller, Henning

2014-01-01

Wearable computing devices are starting to change the way users interact with computers and the Internet. Among them, Google Glass includes a small screen located in front of the right eye, a camera filming in front of the user and a small computing unit. Google Glass has the advantage to provide online services while allowing the user to perform tasks with his/her hands. These augmented glasses uncover many useful applications, also in the medical domain. For example, Google Glass can easily provide video conference between medical doctors to discuss a live case. Using these glasses can also facilitate medical information search by allowing the access of a large amount of annotated medical cases during a consultation in a non-disruptive fashion for medical staff. In this paper, we developed a Google Glass application able to take a photo and send it to a medical image retrieval system along with keywords in order to retrieve similar cases. As a preliminary assessment of the usability of the application, we tested the application under three conditions (images of the skin; printed CT scans and MRI images; and CT and MRI images acquired directly from an LCD screen) to explore whether using Google Glass affects the accuracy of the results returned by the medical image retrieval system. The preliminary results show that despite minor problems due to the relative stability of the Google Glass, images can be sent to and processed by the medical image retrieval system and similar images are returned to the user, potentially helping in the decision making process.

[Estimation of forest canopy chlorophyll content based on PROSPECT and SAIL models].

Science.gov (United States)

Yang, Xi-guang; Fan, Wen-yi; Yu, Ying

2010-11-01

The forest canopy chlorophyll content directly reflects the health and stress of forest. The accurate estimation of the forest canopy chlorophyll content is a significant foundation for researching forest ecosystem cycle models. In the present paper, the inversion of the forest canopy chlorophyll content was based on PROSPECT and SAIL models from the physical mechanism angle. First, leaf spectrum and canopy spectrum were simulated by PROSPECT and SAIL models respectively. And leaf chlorophyll content look-up-table was established for leaf chlorophyll content retrieval. Then leaf chlorophyll content was converted into canopy chlorophyll content by Leaf Area Index (LAD). Finally, canopy chlorophyll content was estimated from Hyperion image. The results indicated that the main effect bands of chlorophyll content were 400-900 nm, the simulation of leaf and canopy spectrum by PROSPECT and SAIL models fit better with the measured spectrum with 7.06% and 16.49% relative error respectively, the RMSE of LAI inversion was 0. 542 6 and the forest canopy chlorophyll content was estimated better by PROSPECT and SAIL models with precision = 77.02%.

Image retrieval by information fusion based on scalable vocabulary tree and robust Hausdorff distance

Science.gov (United States)

Che, Chang; Yu, Xiaoyang; Sun, Xiaoming; Yu, Boyang

2017-12-01

In recent years, Scalable Vocabulary Tree (SVT) has been shown to be effective in image retrieval. However, for general images where the foreground is the object to be recognized while the background is cluttered, the performance of the current SVT framework is restricted. In this paper, a new image retrieval framework that incorporates a robust distance metric and information fusion is proposed, which improves the retrieval performance relative to the baseline SVT approach. First, the visual words that represent the background are diminished by using a robust Hausdorff distance between different images. Second, image matching results based on three image signature representations are fused, which enhances the retrieval precision. We conducted intensive experiments on small-scale to large-scale image datasets: Corel-9, Corel-48, and PKU-198, where the proposed Hausdorff metric and information fusion outperforms the state-of-the-art methods by about 13, 15, and 15%, respectively.

Exploring the Effects of Cloud Vertical Structure on Cloud Microphysical Retrievals based on Polarized Reflectances

Science.gov (United States)

Miller, D. J.; Zhang, Z.; Platnick, S. E.; Ackerman, A. S.; Cornet, C.; Baum, B. A.

2013-12-01

A polarized cloud reflectance simulator was developed by coupling an LES cloud model with a polarized radiative transfer model to assess the capabilities of polarimetric cloud retrievals. With future remote sensing campaigns like NASA's Aerosols/Clouds/Ecosystems (ACE) planning to feature advanced polarimetric instruments it is important for the cloud remote sensing community to understand the retrievable information available and the related systematic/methodical limitations. The cloud retrieval simulator we have developed allows us to probe these important questions in a realistically relevant test bed. Our simulator utilizes a polarized adding-doubling radiative transfer model and an LES cloud field from a DHARMA simulation (Ackerman et al. 2004) with cloud properties based on the stratocumulus clouds observed during the DYCOMS-II field campaign. In this study we will focus on how the vertical structure of cloud microphysics can influence polarized cloud effective radius retrievals. Numerous previous studies have explored how retrievals based on total reflectance are affected by cloud vertical structure (Platnick 2000, Chang and Li 2002) but no such studies about the effects of vertical structure on polarized retrievals exist. Unlike the total cloud reflectance, which is predominantly multiply scattered light, the polarized reflectance is primarily the result of singly scattered photons. Thus the polarized reflectance is sensitive to only the uppermost region of the cloud (tau~influencer on the microphysical development of cloud droplets, can be potentially studied with polarimetric retrievals.

Characterizing the information content of cloud thermodynamic phase retrievals from the notional PACE OCI shortwave reflectance measurements

Science.gov (United States)

Coddington, O. M.; Vukicevic, T.; Schmidt, K. S.; Platnick, S.

2017-08-01

We rigorously quantify the probability of liquid or ice thermodynamic phase using only shortwave spectral channels specific to the National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and the notional future Plankton, Aerosol, Cloud, ocean Ecosystem imager. The results show that two shortwave-infrared channels (2135 and 2250 nm) provide more information on cloud thermodynamic phase than either channel alone; in one case, the probability of ice phase retrieval increases from 65 to 82% by combining 2135 and 2250 nm channels. The analysis is performed with a nonlinear statistical estimation approach, the GEneralized Nonlinear Retrieval Analysis (GENRA). The GENRA technique has previously been used to quantify the retrieval of cloud optical properties from passive shortwave observations, for an assumed thermodynamic phase. Here we present the methodology needed to extend the utility of GENRA to a binary thermodynamic phase space (i.e., liquid or ice). We apply formal information content metrics to quantify our results; two of these (mutual and conditional information) have not previously been used in the field of cloud studies.

Unsupervised symmetrical trademark image retrieval in soccer telecast using wavelet energy and quadtree decomposition

Science.gov (United States)

Ong, Swee Khai; Lim, Wee Keong; Soo, Wooi King

2013-04-01

Trademark, a distinctive symbol, is used to distinguish products or services provided by a particular person, group or organization from other similar entries. As trademark represents the reputation and credit standing of the owner, it is important to differentiate one trademark from another. Many methods have been proposed to identify, classify and retrieve trademarks. However, most methods required features database and sample sets for training prior to recognition and retrieval process. In this paper, a new feature on wavelet coefficients, the localized wavelet energy, is introduced to extract features of trademarks. With this, unsupervised content-based symmetrical trademark image retrieval is proposed without the database and prior training set. The feature analysis is done by an integration of the proposed localized wavelet energy and quadtree decomposed regional symmetrical vector. The proposed framework eradicates the dependence on query database and human participation during the retrieval process. In this paper, trademarks for soccer games sponsors are the intended trademark category. Video frames from soccer telecast are extracted and processed for this study. Reasonably good localization and retrieval results on certain categories of trademarks are achieved. A distinctive symbol is used to distinguish products or services provided by a particular person, group or organization from other similar entries.

Distributed open environment for data retrieval based on pattern recognition techniques

International Nuclear Information System (INIS)

Pereira, A.; Vega, J.; Castro, R.; Portas, A.

2010-01-01

Pattern recognition methods for data retrieval have been applied to fusion databases for the localization and extraction of similar waveforms within temporal evolution signals. In order to standardize the use of these methods, a distributed open environment has been designed. It is based on a client/server architecture that supports distribution, interoperability and portability between heterogeneous platforms. The server part is a single desktop application based on J2EE (Java 2 Enterprise Edition), which provides a mature standard framework and a modular architecture. It can handle transactions and concurrency of components that are deployed on JETTY, an embedded web container within the Java server application for providing HTTP services. The data management is based on Apache DERBY, a relational database engine also embedded on the same Java based solution. This encapsulation allows hiding of unnecessary details about the installation, distribution, and configuration of all these components but with the flexibility to create and allocate many databases on different servers. The DERBY network module increases the scope of the installed database engine by providing traditional Java database network connections (JDBC-TCP/IP). This avoids scattering several database engines (a unique embedded engine defines the rules for accessing the distributed data). Java thin clients (Java 5 or above is the unique requirement) can be executed in the same computer than the server program (for example a desktop computer) but also server and client software can be distributed in a remote participation environment (wide area networks). The thin client provides graphic user interface to look for patterns (entire waveforms or specific structural forms) and display the most similar ones. This is obtained with HTTP requests and by generating dynamic content (servlets) in response to these client requests.

Distributed Open Environment for Data Retrieval based on Pattern Recognition Techniques

Energy Technology Data Exchange (ETDEWEB)

Pereira, A.; Vega, J.; Castro, R.; Portas, A. [Association EuratomCIEMAT para Fusion, Madrid (Spain)

2009-07-01

Full text of publication follows: Pattern recognition methods for data retrieval have been applied to fusion databases for the localization and extraction of similar waveforms within temporal evolution signals. In order to standardize the use of these methods, a distributed open environment has been designed. It is based on a client/server architecture that supports distribution, inter-operability and portability between heterogeneous platforms. The server part is a single desktop application based on J2EE, which provides a mature standard framework and a modular architecture. It can handle transactions and competition of components that are deployed on JETTY, an embedded web container within the Java server application for providing HTTP services. The data management is based on Apache DERBY, a relational database engine also embedded on the same Java based solution. This encapsulation allows concealment of unnecessary details about the installation, distribution, and configuration of all these components but with the flexibility to create and allocate many databases on different servers. The DERBY network module increases the scope of the installed database engine by providing traditional Java database network connections (JDBC-TCP/IP). This avoids scattering several database engines (a unique embedded engine defines the rules for accessing the distributed data). Java thin clients (Java 5 or above is the unique requirement) can be executed in the same computer than the server program (for example a desktop computer) but also server and client software can be distributed in a remote participation environment (wide area networks). The thin client provides graphic user interface to look for patterns (entire waveforms or specific structural forms) and display the most similar ones. This is obtained with HTTP requests and by generating dynamic content (servlets) in response to these client requests. (authors)

Distributed open environment for data retrieval based on pattern recognition techniques

Energy Technology Data Exchange (ETDEWEB)

Pereira, A., E-mail: augusto.pereira@ciemat.e [Asociacion EURATOM/CIEMAT para Fusion, CIEMAT, Edificio 66, Avda. Complutense, 22, 28040 Madrid (Spain); Vega, J.; Castro, R.; Portas, A. [Asociacion EURATOM/CIEMAT para Fusion, CIEMAT, Edificio 66, Avda. Complutense, 22, 28040 Madrid (Spain)

2010-07-15

Pattern recognition methods for data retrieval have been applied to fusion databases for the localization and extraction of similar waveforms within temporal evolution signals. In order to standardize the use of these methods, a distributed open environment has been designed. It is based on a client/server architecture that supports distribution, interoperability and portability between heterogeneous platforms. The server part is a single desktop application based on J2EE (Java 2 Enterprise Edition), which provides a mature standard framework and a modular architecture. It can handle transactions and concurrency of components that are deployed on JETTY, an embedded web container within the Java server application for providing HTTP services. The data management is based on Apache DERBY, a relational database engine also embedded on the same Java based solution. This encapsulation allows hiding of unnecessary details about the installation, distribution, and configuration of all these components but with the flexibility to create and allocate many databases on different servers. The DERBY network module increases the scope of the installed database engine by providing traditional Java database network connections (JDBC-TCP/IP). This avoids scattering several database engines (a unique embedded engine defines the rules for accessing the distributed data). Java thin clients (Java 5 or above is the unique requirement) can be executed in the same computer than the server program (for example a desktop computer) but also server and client software can be distributed in a remote participation environment (wide area networks). The thin client provides graphic user interface to look for patterns (entire waveforms or specific structural forms) and display the most similar ones. This is obtained with HTTP requests and by generating dynamic content (servlets) in response to these client requests.

Phase Retrieval Using a Genetic Algorithm on the Systematic Image-Based Optical Alignment Testbed

Science.gov (United States)

Taylor, Jaime R.

2003-01-01

NASA s Marshall Space Flight Center s Systematic Image-Based Optical Alignment (SIBOA) Testbed was developed to test phase retrieval algorithms and hardware techniques. Individuals working with the facility developed the idea of implementing phase retrieval by breaking the determination of the tip/tilt of each mirror apart from the piston motion (or translation) of each mirror. Presented in this report is an algorithm that determines the optimal phase correction associated only with the piston motion of the mirrors. A description of the Phase Retrieval problem is first presented. The Systematic Image-Based Optical Alignment (SIBOA) Testbeb is then described. A Discrete Fourier Transform (DFT) is necessary to transfer the incoming wavefront (or estimate of phase error) into the spatial frequency domain to compare it with the image. A method for reducing the DFT to seven scalar/matrix multiplications is presented. A genetic algorithm is then used to search for the phase error. The results of this new algorithm on a test problem are presented.

Intelligent image retrieval based on radiology reports

Energy Technology Data Exchange (ETDEWEB)

Gerstmair, Axel; Langer, Mathias; Kotter, Elmar [University Medical Center Freiburg, Department of Diagnostic Radiology, Freiburg (Germany); Daumke, Philipp; Simon, Kai [Averbis GmbH, Freiburg (Germany)

2012-12-15

To create an advanced image retrieval and data-mining system based on in-house radiology reports. Radiology reports are semantically analysed using natural language processing (NLP) techniques and stored in a state-of-the-art search engine. Images referenced by sequence and image number in the reports are retrieved from the picture archiving and communication system (PACS) and stored for later viewing. A web-based front end is used as an interface to query for images and show the results with the retrieved images and report text. Using a comprehensive radiological lexicon for the underlying terminology, the search algorithm also finds results for synonyms, abbreviations and related topics. The test set was 108 manually annotated reports analysed by different system configurations. Best results were achieved using full syntactic and semantic analysis with a precision of 0.929 and recall of 0.952. Operating successfully since October 2010, 258,824 reports have been indexed and a total of 405,146 preview images are stored in the database. Data-mining and NLP techniques provide quick access to a vast repository of images and radiology reports with both high precision and recall values. Consequently, the system has become a valuable tool in daily clinical routine, education and research. (orig.)

Intelligent image retrieval based on radiology reports

International Nuclear Information System (INIS)

Gerstmair, Axel; Langer, Mathias; Kotter, Elmar; Daumke, Philipp; Simon, Kai

2012-01-01

To create an advanced image retrieval and data-mining system based on in-house radiology reports. Radiology reports are semantically analysed using natural language processing (NLP) techniques and stored in a state-of-the-art search engine. Images referenced by sequence and image number in the reports are retrieved from the picture archiving and communication system (PACS) and stored for later viewing. A web-based front end is used as an interface to query for images and show the results with the retrieved images and report text. Using a comprehensive radiological lexicon for the underlying terminology, the search algorithm also finds results for synonyms, abbreviations and related topics. The test set was 108 manually annotated reports analysed by different system configurations. Best results were achieved using full syntactic and semantic analysis with a precision of 0.929 and recall of 0.952. Operating successfully since October 2010, 258,824 reports have been indexed and a total of 405,146 preview images are stored in the database. Data-mining and NLP techniques provide quick access to a vast repository of images and radiology reports with both high precision and recall values. Consequently, the system has become a valuable tool in daily clinical routine, education and research. (orig.)

Model-based magnetization retrieval from holographic phase images

Energy Technology Data Exchange (ETDEWEB)

Röder, Falk, E-mail: f.roeder@hzdr.de [Helmholtz-Zentrum Dresden-Rossendorf, Institut für Ionenstrahlphysik und Materialforschung, Bautzner Landstr. 400, D-01328 Dresden (Germany); Triebenberg Labor, Institut für Strukturphysik, Technische Universität Dresden, D-01062 Dresden (Germany); Vogel, Karin [Triebenberg Labor, Institut für Strukturphysik, Technische Universität Dresden, D-01062 Dresden (Germany); Wolf, Daniel [Helmholtz-Zentrum Dresden-Rossendorf, Institut für Ionenstrahlphysik und Materialforschung, Bautzner Landstr. 400, D-01328 Dresden (Germany); Triebenberg Labor, Institut für Strukturphysik, Technische Universität Dresden, D-01062 Dresden (Germany); Hellwig, Olav [Helmholtz-Zentrum Dresden-Rossendorf, Institut für Ionenstrahlphysik und Materialforschung, Bautzner Landstr. 400, D-01328 Dresden (Germany); AG Magnetische Funktionsmaterialien, Institut für Physik, Technische Universität Chemnitz, D-09126 Chemnitz (Germany); HGST, A Western Digital Company, 3403 Yerba Buena Rd., San Jose, CA 95135 (United States); Wee, Sung Hun [HGST, A Western Digital Company, 3403 Yerba Buena Rd., San Jose, CA 95135 (United States); Wicht, Sebastian; Rellinghaus, Bernd [IFW Dresden, Institute for Metallic Materials, P.O. Box 270116, D-01171 Dresden (Germany)

2017-05-15

The phase shift of the electron wave is a useful measure for the projected magnetic flux density of magnetic objects at the nanometer scale. More important for materials science, however, is the knowledge about the magnetization in a magnetic nano-structure. As demonstrated here, a dominating presence of stray fields prohibits a direct interpretation of the phase in terms of magnetization modulus and direction. We therefore present a model-based approach for retrieving the magnetization by considering the projected shape of the nano-structure and assuming a homogeneous magnetization therein. We apply this method to FePt nano-islands epitaxially grown on a SrTiO{sub 3} substrate, which indicates an inclination of their magnetization direction relative to the structural easy magnetic [001] axis. By means of this real-world example, we discuss prospects and limits of this approach. - Highlights: • Retrieval of the magnetization from holographic phase images. • Magnetostatic model constructed for a magnetic nano-structure. • Decomposition into homogeneously magnetized components. • Discretization of a each component by elementary cuboids. • Analytic solution for the phase of a magnetized cuboid considered. • Fitting a set of magnetization vectors to experimental phase images.

Validity evidence based on test content.

Science.gov (United States)

Sireci, Stephen; Faulkner-Bond, Molly

2014-01-01

Validity evidence based on test content is one of the five forms of validity evidence stipulated in the Standards for Educational and Psychological Testing developed by the American Educational Research Association, American Psychological Association, and National Council on Measurement in Education. In this paper, we describe the logic and theory underlying such evidence and describe traditional and modern methods for gathering and analyzing content validity data. A comprehensive review of the literature and of the aforementioned Standards is presented. For educational tests and other assessments targeting knowledge and skill possessed by examinees, validity evidence based on test content is necessary for building a validity argument to support the use of a test for a particular purpose. By following the methods described in this article, practitioners have a wide arsenal of tools available for determining how well the content of an assessment is congruent with and appropriate for the specific testing purposes.

Interactions among emotional attention, encoding, and retrieval of ambiguous information: An eye-tracking study.

Science.gov (United States)

Everaert, Jonas; Koster, Ernst H W

2015-10-01

Emotional biases in attention modulate encoding of emotional material into long-term memory, but little is known about the role of such attentional biases during emotional memory retrieval. The present study investigated how emotional biases in memory are related to attentional allocation during retrieval. Forty-nine individuals encoded emotionally positive and negative meanings derived from ambiguous information and then searched their memory for encoded meanings in response to a set of retrieval cues. The remember/know/new procedure was used to classify memories as recollection-based or familiarity-based, and gaze behavior was monitored throughout the task to measure attentional allocation. We found that a bias in sustained attention during recollection-based, but not familiarity-based, retrieval predicted subsequent memory bias toward positive versus negative material following encoding. Thus, during emotional memory retrieval, attention affects controlled forms of retrieval (i.e., recollection) but does not modulate relatively automatic, familiarity-based retrieval. These findings enhance understanding of how distinct components of attention regulate the emotional content of memories. Implications for theoretical models and emotion regulation are discussed. (c) 2015 APA, all rights reserved).

A LDA-based approach to promoting ranking diversity for genomics information retrieval.

Science.gov (United States)

Chen, Yan; Yin, Xiaoshi; Li, Zhoujun; Hu, Xiaohua; Huang, Jimmy Xiangji

2012-06-11

In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc. Hence, it is important of a biomedical IR system to be able to provide relevant and diverse answers to fulfill biologists' information needs. However traditional IR model only concerns with the relevance between retrieved documents and user query, but does not take redundancy between retrieved documents into account. This will lead to high redundancy and low diversity in the retrieval ranked lists. In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. We perform our approach on TREC 2007 Genomics collection and two distinctive IR baseline runs, which can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track. The proposed method is the first study of adopting topic model to genomics information retrieval, and demonstrates its effectiveness in promoting ranking diversity as well as in improving relevance of ranked lists of genomics search

Experimental evaluation of ontology-based HIV/AIDS frequently asked question retrieval system.

Science.gov (United States)

Ayalew, Yirsaw; Moeng, Barbara; Mosweunyane, Gontlafetse

2018-05-01

This study presents the results of experimental evaluations of an ontology-based frequently asked question retrieval system in the domain of HIV and AIDS. The main purpose of the system is to provide answers to questions on HIV/AIDS using ontology. To evaluate the effectiveness of the frequently asked question retrieval system, we conducted two experiments. The first experiment focused on the evaluation of the quality of the ontology we developed using the OQuaRE evaluation framework which is based on software quality metrics and metrics designed for ontology quality evaluation. The second experiment focused on evaluating the effectiveness of the ontology in retrieving relevant answers. For this we used an open-source information retrieval platform, Terrier, with retrieval models BM25 and PL2. For the measurement of performance, we used the measures mean average precision, mean reciprocal rank, and precision at 5. The results suggest that frequently asked question retrieval with ontology is more effective than frequently asked question retrieval without ontology in the domain of HIV/AIDS.

Functional alarming and information retrieval

International Nuclear Information System (INIS)

Goodstein, L.P.

1985-08-01

This paper deals with two facets of the design and efficient utilisation by operating personnel of computer-based interfaces for monitoring and the supervisory control of complex industrial systems - e.g., power stations, chemical plants, etc. These are alarming and information retrieval both of which are extremely sensitive to computerisation. For example, the advent of computers for display requires that some means of assuring easy and rapid access to large amounts of relevant stored information be found. In this paper, alarming and information retrieval are linked together through a multilevel functional description of the target plant. This representation serves as a framework for structuring the access to information as well as defining associated ''alarms'' at the various descriptive levels. Particular attention is paid to the level where mass and energy flows and balances are relevant. It is shown that the number of alarms here is reduced considerably while information about content and interrelationships is enhanced - which at the same time eases the retrieval problem. (author)

Multi-instance learning based on instance consistency for image retrieval

Science.gov (United States)

Zhang, Miao; Wu, Zhize; Wan, Shouhong; Yue, Lihua; Yin, Bangjie

2017-07-01

Multiple-instance learning (MIL) has been successfully utilized in image retrieval. Existing approaches cannot select positive instances correctly from positive bags which may result in a low accuracy. In this paper, we propose a new image retrieval approach called multiple instance learning based on instance-consistency (MILIC) to mitigate such issue. First, we select potential positive instances effectively in each positive bag by ranking instance-consistency (IC) values of instances. Then, we design a feature representation scheme, which can represent the relationship among bags and instances, based on potential positive instances to convert a bag into a single instance. Finally, we can use a standard single-instance learning strategy, such as the support vector machine, for performing object-based image retrieval. Experimental results on two challenging data sets show the effectiveness of our proposal in terms of accuracy and run time.

A novel biomedical image indexing and retrieval system via deep preference learning.

Science.gov (United States)

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state

Improvements of a COMS Land Surface Temperature Retrieval Algorithm Based on the Temperature Lapse Rate and Water Vapor/Aerosol Effect

Directory of Open Access Journals (Sweden)

A-Ra Cho

2015-02-01

Full Text Available The National Meteorological Satellite Center in Korea retrieves land surface temperature (LST by applying the split-window LST algorithm (CSW_v1.0 to Communication, Ocean, and Meteorological Satellite (COMS data. Considerable errors were detected under conditions of high water vapor content or temperature lapse rates during validation with Moderate Resolution Imaging Spectroradiometer (MODIS LST because of the too simplified LST algorithm. In this study, six types of LST retrieval equations (CSW_v2.0 were developed to upgrade the CSW_v1.0. These methods were developed by classifying “dry,” “normal,” and “wet” cases for day and night and considering the relative sizes of brightness temperature difference (BTD values. Similar to CSW_v1.0, the LST retrieved by CSW_v2.0 had a correlation coefficient of 0.99 with the prescribed LST and a slightly larger bias of −0.03 K from 0.00K; the root mean square error (RMSE improved from 1.41 K to 1.39 K. In general, CSW_v2.0 improved the retrieval accuracy compared to CSW_v1.0, especially when the lapse rate was high (mid-day and dawn and the water vapor content was high. The spatial distributions of LST retrieved by CSW_v2.0 were found to be similar to the MODIS LST independently of the season, day/night, and geographic locations. The validation using one year’s MODIS LST data showed that CSW_v2.0 improved the retrieval accuracy of LST in terms of correlations (from 0.988 to 0.989, bias (from −1.009 K to 0.292 K, and RMSEs (from 2.613 K to 2.237 K.

Retrieval of canopy moisture content for dynamic fire risk assessment using simulated MODIS bands

Science.gov (United States)

Maffei, Carmine; Leone, Antonio P.; Meoli, Giuseppe; Calabrò, Gaetano; Menenti, Massimo

2007-10-01

Forest fires are one of the major environmental hazards in Mediterranean Europe. Biomass burning reduces carbon fixation in terrestrial vegetation, while soil erosion increases in burned areas. For these reasons, more sophisticated prevention tools are needed by local authorities to forecast fire danger, allowing a sound allocation of intervention resources. Various factors contribute to the quantification of fire hazard, and among them vegetation moisture is the one that dictates vegetation susceptibility to fire ignition and propagation. Many authors have demonstrated the role of remote sensing in the assessment of vegetation equivalent water thickness (EWT), which is defined as the weight of liquid water per unit of leaf surface. However, fire models rely on the fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the weight of the liquid water in a leaf over the weight of dry matter, and its retrieval from remote sensing measurements might be problematic, since it is calculated from two biophysical properties that independently affect vegetation reflectance spectrum. The aim of this research is to evaluate the potential of the Moderate Resolution Imaging Spectrometer (MODIS) in retrieving both EWT and FMC from top of the canopy reflectance. The PROSPECT radiative transfer code was used to simulate leaf reflectance and transmittance as a function of leaf properties, and the SAILH model was adopted to simulate the top of the canopy reflectance. A number of moisture spectral indexes have been calculated, based on MODIS bands, and their performance in predicting EWT and FMC has been evaluated. Results showed that traditional moisture spectral indexes can accurately predict EWT but not FMC. However, it has been found that it is possible to take advantage of the multiple MODIS short-wave infrared (SWIR) channels to improve the retrieval accuracy of FMC (r2 = 0.73). The effects of canopy structural properties on MODIS

A Typed Text Retrieval Query Language for XML Documents.

Science.gov (United States)

Colazzo, Dario; Sartiani, Carlo; Albano, Antonio; Manghi, Paolo; Ghelli, Giorgio; Lini, Luca; Paoli, Michele

2002-01-01

Discussion of XML focuses on a description of Tequyla-TX, a typed text retrieval query language for XML documents that can search on both content and structures. Highlights include motivations; numerous examples; word-based and char-based searches; tag-dependent full-text searches; text normalization; query algebra; data models and term language;…

A new method for assessing content validity in model-based creation and iteration of eHealth interventions.

Science.gov (United States)

Kassam-Adams, Nancy; Marsac, Meghan L; Kohser, Kristen L; Kenardy, Justin A; March, Sonja; Winston, Flaura K

2015-04-15

The advent of eHealth interventions to address psychological concerns and health behaviors has created new opportunities, including the ability to optimize the effectiveness of intervention activities and then deliver these activities consistently to a large number of individuals in need. Given that eHealth interventions grounded in a well-delineated theoretical model for change are more likely to be effective and that eHealth interventions can be costly to develop, assuring the match of final intervention content and activities to the underlying model is a key step. We propose to apply the concept of "content validity" as a crucial checkpoint to evaluate the extent to which proposed intervention activities in an eHealth intervention program are valid (eg, relevant and likely to be effective) for the specific mechanism of change that each is intended to target and the intended target population for the intervention. The aims of this paper are to define content validity as it applies to model-based eHealth intervention development, to present a feasible method for assessing content validity in this context, and to describe the implementation of this new method during the development of a Web-based intervention for children. We designed a practical 5-step method for assessing content validity in eHealth interventions that includes defining key intervention targets, delineating intervention activity-target pairings, identifying experts and using a survey tool to gather expert ratings of the relevance of each activity to its intended target, its likely effectiveness in achieving the intended target, and its appropriateness with a specific intended audience, and then using quantitative and qualitative results to identify intervention activities that may need modification. We applied this method during our development of the Coping Coach Web-based intervention for school-age children. In the evaluation of Coping Coach content validity, 15 experts from five countries

The effect of cloud screening on MAX-DOAS aerosol retrievals.

Science.gov (United States)

Gielen, Clio; Van Roozendael, Michel; Hendrik, Francois; Fayt, Caroline; Hermans, Christian; Pinardi, Gaia; De Backer, Hugo; De Bock, Veerle; Laffineur, Quentin; Vlemmix, Tim

2014-05-01

In recent years, ground-based multi-axis differential absorption spectroscopy (MAX-DOAS) has shown to be ideally suited for the retrieval of tropospheric trace gases and deriving information on the aerosol properties. These measurements are invaluable to our understanding of the physics and chemistry of the atmospheric system, and the impact on the Earth's climate. Unfortunately, MAX-DOAS measurements are often performed under strong non-clear-sky conditions, causing strong data quality degradation and uncertainties on the retrievals. Here we present the result of our cloud-screening method, using the colour index (CI), on aerosol retrievals from MAX-DOAS measurements (AOD and vertical profiles). We focus on two large data sets, from the Brussels and Beijing area. Using the CI we define 3 different sky conditions: bad (=full thick cloud cover/extreme aerosols), mediocre (=thin clouds/aerosols) and good (=clear sky). We also flag the presence of broken/scattered clouds. We further compare our cloud-screening method with results from cloud-cover fractions derived from thermic infrared measurements. In general, our method shows good results to qualify the sky and cloud conditions of MAX-DOAS measurements, without the need for other external cloud-detection systems. Removing data under bad-sky and broken-cloud conditions results in a strongly improved agreement, in both correlation and slope, between the MAX-DOAS aerosol retrievals and data from other instruments (e.g. AERONET, Brewer). With the improved AOD retrievals, the seasonal and diurnal variations of the aerosol content and vertical distribution at both sites can be investigated in further detail. By combining with additional information derived by other instruments (Brewer, lidar, ...) operated at the stations, we will further study the observed aerosol characteristics, and their influence on and by meteorological conditions such as clouds and/or the boundary layer height.

[Study of Determination of Oil Mixture Components Content Based on Quasi-Monte Carlo Method].

Science.gov (United States)

Wang, Yu-tian; Xu, Jing; Liu, Xiao-fei; Chen, Meng-han; Wang, Shi-tao

2015-05-01

Gasoline, kerosene, diesel is processed by crude oil with different distillation range. The boiling range of gasoline is 35 ~205 °C. The boiling range of kerosene is 140~250 °C. And the boiling range of diesel is 180~370 °C. At the same time, the carbon chain length of differentmineral oil is different. The carbon chain-length of gasoline is within the scope of C7 to C11. The carbon chain length of kerosene is within the scope of C12 to C15. And the carbon chain length of diesel is within the scope of C15 to C18. The recognition and quantitative measurement of three kinds of mineral oil is based on different fluorescence spectrum formed in their different carbon number distribution characteristics. Mineral oil pollution occurs frequently, so monitoring mineral oil content in the ocean is very important. A new method of components content determination of spectra overlapping mineral oil mixture is proposed, with calculation of characteristic peak power integrationof three-dimensional fluorescence spectrum by using Quasi-Monte Carlo Method, combined with optimal algorithm solving optimum number of characteristic peak and range of integral region, solving nonlinear equations by using BFGS(a rank to two update method named after its inventor surname first letter, Boyden, Fletcher, Goldfarb and Shanno) method. Peak power accumulation of determined points in selected area is sensitive to small changes of fluorescence spectral line, so the measurement of small changes of component content is sensitive. At the same time, compared with the single point measurement, measurement sensitivity is improved by the decrease influence of random error due to the selection of points. Three-dimensional fluorescence spectra and fluorescence contour spectra of single mineral oil and the mixture are measured by taking kerosene, diesel and gasoline as research objects, with a single mineral oil regarded whole, not considered each mineral oil components. Six characteristic peaks are

Benchmarking, Research, Development, and Support for ORNL Automated Image and Signature Retrieval (AIR/ASR) Technologies

Energy Technology Data Exchange (ETDEWEB)

Tobin, K.W.

2004-06-01

This report describes the results of a Cooperative Research and Development Agreement (CRADA) with Applied Materials, Inc. (AMAT) of Santa Clara, California. This project encompassed the continued development and integration of the ORNL Automated Image Retrieval (AIR) technology, and an extension of the technology denoted Automated Signature Retrieval (ASR), and other related technologies with the Defect Source Identification (DSI) software system that was under development by AMAT at the time this work was performed. In the semiconductor manufacturing environment, defect imagery is used to diagnose problems in the manufacturing line, train yield management engineers, and examine historical data for trends. Image management in semiconductor data systems is a growing cause of concern in the industry as fabricators are now collecting up to 20,000 images each week. In response to this concern, researchers at the Oak Ridge National Laboratory (ORNL) developed a semiconductor-specific content-based image retrieval method and system, also known as AIR. The system uses an image-based query-by-example method to locate and retrieve similar imagery from a database of digital imagery using visual image characteristics. The query method is based on a unique architecture that takes advantage of the statistical, morphological, and structural characteristics of image data, generated by inspection equipment in industrial applications. The system improves the manufacturing process by allowing rapid access to historical records of similar events so that errant process equipment can be isolated and corrective actions can be quickly taken to improve yield. The combined ORNL and AMAT technology is referred to hereafter as DSI-AIR and DSI-ASR.

Hybrid iterative phase retrieval algorithm based on fusion of intensity information in three defocused planes.

Science.gov (United States)

Zeng, Fa; Tan, Qiaofeng; Yan, Yingbai; Jin, Guofan

2007-10-01

Study of phase retrieval technology is quite meaningful, for its wide applications related to many domains, such as adaptive optics, detection of laser quality, precise measurement of optical surface, and so on. Here a hybrid iterative phase retrieval algorithm is proposed, based on fusion of the intensity information in three defocused planes. First the conjugate gradient algorithm is adapted to achieve a coarse solution of phase distribution in the input plane; then the iterative angular spectrum method is applied in succession for better retrieval result. This algorithm is still applicable even when the exact shape and size of the aperture in the input plane are unknown. Moreover, this algorithm always exhibits good convergence, i.e., the retrieved results are insensitive to the chosen positions of the three defocused planes and the initial guess of complex amplitude in the input plane, which has been proved by both simulations and further experiments.

Care episode retrieval: distributional semantic models for information retrieval in the clinical domain.

Science.gov (United States)

Moen, Hans; Ginter, Filip; Marsi, Erwin; Peltonen, Laura-Maria; Salakoski, Tapio; Salanterä, Sanna

2015-01-01

Patients' health related information is stored in electronic health records (EHRs) by health service providers. These records include sequential documentation of care episodes in the form of clinical notes. EHRs are used throughout the health care sector by professionals, administrators and patients, primarily for clinical purposes, but also for secondary purposes such as decision support and research. The vast amounts of information in EHR systems complicate information management and increase the risk of information overload. Therefore, clinicians and researchers need new tools to manage the information stored in the EHRs. A common use case is, given a--possibly unfinished--care episode, to retrieve the most similar care episodes among the records. This paper presents several methods for information retrieval, focusing on care episode retrieval, based on textual similarity, where similarity is measured through domain-specific modelling of the distributional semantics of words. Models include variants of random indexing and the semantic neural network model word2vec. Two novel methods are introduced that utilize the ICD-10 codes attached to care episodes to better induce domain-specificity in the semantic model. We report on experimental evaluation of care episode retrieval that circumvents the lack of human judgements regarding episode relevance. Results suggest that several of the methods proposed outperform a state-of-the art search engine (Lucene) on the retrieval task.

Retrieval of tropospheric carbon monoxide for the MOPITT experiment

Science.gov (United States)

Pan, Liwen; Gille, John C.; Edwards, David P.; Bailey, Paul L.; Rodgers, Clive D.

1998-12-01

A retrieval method for deriving the tropospheric carbon monoxide (CO) profile and column amount under clear sky conditions has been developed for the Measurements of Pollution In The Troposphere (MOPITT) instrument, scheduled for launch in 1998 onboard the EOS-AM1 satellite. This paper presents a description of the method along with analyses of retrieval information content. These analyses characterize the forward measurement sensitivity, the contribution of a priori information, and the retrieval vertical resolution. Ensembles of tropospheric CO profiles were compiled both from aircraft in situ measurements and from chemical model results and were used in retrieval experiments to characterize the method and to study the sensitivity to different parameters. Linear error analyses were carried out in parallel with the ensemble experiments. Results of these experiments and analyses indicate that MOPITT CO column measurements will have better than 10% precision, and CO profile measurement will have approximately three pieces of independent information that will resolve 3-5 tropospheric layers to approximately 10% precision. These analyses are important for understanding MOPITT data, both for application of data in tropospheric chemistry studies and for comparison with in situ measurements.

Exploring The Limits Of Variational Passive Microwave Retrievals

Science.gov (United States)

Duncan, David Ian

Passive microwave observations from satellite platforms constitute one of the most important data records of the global observing system. Operational since the late 1970s, passive microwave data underpin climate records of precipitation, sea ice extent, water vapor, and more, and contribute significantly to numerical weather prediction via data assimilation. Detailed understanding of the observation errors in these data is key to maximizing their utility for research and operational applications alike. However, the treatment of observation errors in this data record has been lacking and somewhat divergent when considering the retrieval and data assimilation communities. In this study, some limits of passive microwave imager data are considered in light of more holistic treatment of observation errors. A variational retrieval, named the CSU 1DVAR, was developed for microwave imagers and applied to the GMI and AMSR2 sensors for ocean scenes. Via an innovative method to determine forward model error, this retrieval accounts for error covariances across all channels used in the iteration. This improves validation in more complex scenes such as high wind speed and persistently cloudy regimes. In addition, it validates on par with a benchmark dataset without any tuning to in-situ observations. The algorithm yields full posterior error diagnostics and its physical forward model is applicable to other sensors, pending intercalibration. This retrieval is used to explore the viability of retrieving parameters at the limits of the available information content from a typical microwave imager. Retrieval of warm rain, marginal sea ice, and falling snow are explored with the variational retrieval. Warm rain retrieval shows some promise, with greater sensitivity than operational GPM algorithms due to leveraging CloudSat data and accounting for drop size distribution variability. Marginal sea ice is also detected with greater sensitivity than a standard operational retrieval

Speech Transduction Based on Linguistic Content

DEFF Research Database (Denmark)

Juel Henrichsen, Peter; Christiansen, Thomas Ulrich

Digital hearing aids use a variety of advanced digital signal processing methods in order to improve speech intelligibility. These methods are based on knowledge about the acoustics outside the ear as well as psychoacoustics. This paper investigates the recent observation that speech elements...... with a high degree of information can be robustly identified based on basic acoustic properties, i.e., function words have greater spectral tilt than content words for each of the 18 Danish talkers investigated. In this paper we examine these spectral tilt differences as a function of time based on a speech...... material six times the duration of previous investigations. Our results show that the correlation of spectral tilt with information content is relatively constant across time, even if averaged across talkers. This indicates that it is possible to devise a robust method for estimating information density...

Rotation Invariant Color Retrieval

OpenAIRE

Swapna Borde; Udhav Bhosle

2013-01-01

The new technique for image retrieval using the color features extracted from images based on LogHistogram is proposed. The proposed technique is compared with Global color histogram and histogram ofcorners .It has been observed that number of histogram bins used for retrieval comparison of proposedtechnique (Log Histogram)is less as compared to Global Color Histogram and Histogram of corners. Theexperimental results on a database of 792 images with 11 classes indicate that proposed method (L...

Retrieve polarization aberration from image degradation: a new measurement method in DUV lithography

Science.gov (United States)

Xiang, Zhongbo; Li, Yanqiu

2017-10-01

Detailed knowledge of polarization aberration (PA) of projection lens in higher-NA DUV lithographic imaging is necessary due to its impact to imaging degradations, and precise measurement of PA is conductive to computational lithography techniques such as RET and OPC. Current in situ measurement method of PA thorough the detection of degradations of aerial images need to do linear approximation and apply the assumption of 3-beam/2-beam interference condition. The former approximation neglects the coupling effect of the PA coefficients, which would significantly influence the accuracy of PA retrieving. The latter assumption restricts the feasible pitch of test masks in higher-NA system, conflicts with the Kirhhoff diffraction model of test mask used in retrieving model, and introduces 3D mask effect as a source of retrieving error. In this paper, a new in situ measurement method of PA is proposed. It establishes the analytical quadratic relation between the PA coefficients and the degradations of aerial images of one-dimensional dense lines in coherent illumination through vector aerial imaging, which does not rely on the assumption of 3-beam/2- beam interference and linear approximation. In this case, the retrieval of PA from image degradation can be convert from the nonlinear system of m-quadratic equations to a multi-objective quadratic optimization problem, and finally be solved by nonlinear least square method. Some preliminary simulation results are given to demonstrate the correctness and accuracy of the new PA retrieving model.

Semantic Indexing and Retrieval based on Formal Concept Analysis

OpenAIRE

Codocedo , Victor; Lykourentzou , Ioanna; Napoli , Amedeo

2012-01-01

Semantic indexing and retrieval has become an important research area, as the available amount of information on the Web is growing more and more. In this paper, we introduce an original approach to semantic indexing and retrieval based on Formal Concept Analysis. The concept lattice is used as a semantic index and we propose an original algorithm for traversing the lattice and answering user queries. This framework has been used and evaluated on song datasets.

Exploring Database Improvements for GPM Constellation Precipitation Retrievals

Science.gov (United States)

Ringerud, S.; Kidd, C.; Skofronick Jackson, G.

2017-12-01

The Global Precipitation Measurement Mission (GPM) offers an unprecedented opportunity for understanding and mapping of liquid and frozen precipitation on a global scale. GPM mission development of physically based retrieval algorithms, for application consistently across the constellation radiometers, relies on combined active-passive retrievals from the GPM core satellite as a transfer standard. Radiative transfer modeling is then utilized to compute a priori databases at the frequency and footprint geometry of each individual radiometer. The Goddard Profiling Algorithm (GPROF) performs constellation retrievals across the GPM databases in a Bayesian framework, constraining searches using model data on a pixel-by-pixel basis. This work explores how the retrieval might be enhanced with additional information available within the brightness temperature observations themselves. In order to better exploit available information content, model water vapor is replaced with retrieved water vapor. Rather than treating each footprint as a 1D profile alone in space, information regarding Tb variability in the horizontal is added as well as variability in the time dimension. This additional information is tested and evaluated for retrieval improvement in the context of the Bayesian retrieval scheme. Retrieval differences are presented as a function of precipitation and surface type for evaluation of where the added information proves most effective.

[Method of traditional Chinese medicine formula design based on 3D-database pharmacophore search and patent retrieval].

Science.gov (United States)

He, Yu-su; Sun, Zhi-yi; Zhang, Yan-ling

2014-11-01

By using the pharmacophore model of mineralocorticoid receptor antagonists as a starting point, the experiment stud- ies the method of traditional Chinese medicine formula design for anti-hypertensive. Pharmacophore models were generated by 3D-QSAR pharmacophore (Hypogen) program of the DS3.5, based on the training set composed of 33 mineralocorticoid receptor antagonists. The best pharmacophore model consisted of two Hydrogen-bond acceptors, three Hydrophobic and four excluded volumes. Its correlation coefficient of training set and test set, N, and CAI value were 0.9534, 0.6748, 2.878, and 1.119. According to the database screening, 1700 active compounds from 86 source plant were obtained. Because of lacking of available anti-hypertensive medi cation strategy in traditional theory, this article takes advantage of patent retrieval in world traditional medicine patent database, in order to design drug formula. Finally, two formulae was obtained for antihypertensive.

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves.

Science.gov (United States)

Pérez-Patricio, Madaín; Camas-Anzueto, Jorge Luis; Sanchez-Alegría, Avisaí; Aguilar-González, Abiel; Gutiérrez-Miceli, Federico; Escobar-Gómez, Elías; Voisin, Yvon; Rios-Rojas, Carlos; Grajales-Coutiño, Ruben

2018-02-22

This work introduces a new vision-based approach for estimating chlorophyll contents in a plant leaf using reflectance and transmittance as base parameters. Images of the top and underside of the leaf are captured. To estimate the base parameters (reflectance/transmittance), a novel optical arrangement is proposed. The chlorophyll content is then estimated by using linear regression where the inputs are the reflectance and transmittance of the leaf. Performance of the proposed method for chlorophyll content estimation was compared with a spectrophotometer and a Soil Plant Analysis Development (SPAD) meter. Chlorophyll content estimation was realized for Lactuca sativa L., Azadirachta indica , Canavalia ensiforme , and Lycopersicon esculentum . Experimental results showed that-in terms of accuracy and processing speed-the proposed algorithm outperformed many of the previous vision-based approach methods that have used SPAD as a reference device. On the other hand, the accuracy reached is 91% for crops such as Azadirachta indica , where the chlorophyll value was obtained using the spectrophotometer. Additionally, it was possible to achieve an estimation of the chlorophyll content in the leaf every 200 ms with a low-cost camera and a simple optical arrangement. This non-destructive method increased accuracy in the chlorophyll content estimation by using an optical arrangement that yielded both the reflectance and transmittance information, while the required hardware is cheap.

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves

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Madaín Pérez-Patricio

2018-02-01

Full Text Available This work introduces a new vision-based approach for estimating chlorophyll contents in a plant leaf using reflectance and transmittance as base parameters. Images of the top and underside of the leaf are captured. To estimate the base parameters (reflectance/transmittance, a novel optical arrangement is proposed. The chlorophyll content is then estimated by using linear regression where the inputs are the reflectance and transmittance of the leaf. Performance of the proposed method for chlorophyll content estimation was compared with a spectrophotometer and a Soil Plant Analysis Development (SPAD meter. Chlorophyll content estimation was realized for Lactuca sativa L., Azadirachta indica, Canavalia ensiforme, and Lycopersicon esculentum. Experimental results showed that—in terms of accuracy and processing speed—the proposed algorithm outperformed many of the previous vision-based approach methods that have used SPAD as a reference device. On the other hand, the accuracy reached is 91% for crops such as Azadirachta indica, where the chlorophyll value was obtained using the spectrophotometer. Additionally, it was possible to achieve an estimation of the chlorophyll content in the leaf every 200 ms with a low-cost camera and a simple optical arrangement. This non-destructive method increased accuracy in the chlorophyll content estimation by using an optical arrangement that yielded both the reflectance and transmittance information, while the required hardware is cheap.

Using background knowledge for picture organization and retrieval

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Quintana, Yuri

1997-01-01

A picture knowledge base management system is described that is used to represent, organize and retrieve pictures from a frame knowledge base. Experiments with human test subjects were conducted to obtain further descriptions of pictures from news magazines. These descriptions were used to represent the semantic content of pictures in frame representations. A conceptual clustering algorithm is described which organizes pictures not only on the observable features, but also on implicit properties derived from the frame representations. The algorithm uses inheritance reasoning to take into account background knowledge in the clustering. The algorithm creates clusters of pictures using a group similarity function that is based on the gestalt theory of picture perception. For each cluster created, a frame is generated which describes the semantic content of pictures in the cluster. Clustering and retrieval experiments were conducted with and without background knowledge. The paper shows how the use of background knowledge and semantic similarity heuristics improves the speed, precision, and recall of queries processed. The paper concludes with a discussion of how natural language processing of can be used to assist in the development of knowledge bases and the processing of user queries.

Bread Water Content Measurement Based on Hyperspectral Imaging

DEFF Research Database (Denmark)

Liu, Zhi; Møller, Flemming

2011-01-01

Water content is one of the most important properties of the bread for tasting assesment or store monitoring. Traditional bread water content measurement methods mostly are processed manually, which is destructive and time consuming. This paper proposes an automated water content measurement...... for bread quality based on near-infrared hyperspectral imaging against the conventional manual loss-in-weight method. For this purpose, the hyperspectral components unmixing technology is used for measuring the water content quantitatively. And the definition on bread water content index is presented...

Spatial Pyramid Covariance based Compact Video Code for Robust Face Retrieval in TV-series.

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Li, Yan; Wang, Ruiping; Cui, Zhen; Shan, Shiguang; Chen, Xilin

2016-10-10

We address the problem of face video retrieval in TV-series which searches video clips based on the presence of specific character, given one face track of his/her. This is tremendously challenging because on one hand, faces in TV-series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand retrieval task typically needs efficient representation with low time and space complexity. To handle this problem, we propose a compact and discriminative representation for the huge body of video data, named Compact Video Code (CVC). Our method first models the face track by its sample (i.e., frame) covariance matrix to capture the video data variations in a statistical manner. To incorporate discriminative information and obtain more compact video signature suitable for retrieval, the high-dimensional covariance representation is further encoded as a much lower-dimensional binary vector, which finally yields the proposed CVC. Specifically, each bit of the code, i.e., each dimension of the binary vector, is produced via supervised learning in a max margin framework, which aims to make a balance between the discriminability and stability of the code. Besides, we further extend the descriptive granularity of covariance matrix from traditional pixel-level to more general patchlevel, and proceed to propose a novel hierarchical video representation named Spatial Pyramid Covariance (SPC) along with a fast calculation method. Face retrieval experiments on two challenging TV-series video databases, i.e., the Big Bang Theory and Prison Break, demonstrate the competitiveness of the proposed CVC over state-of-the-art retrieval methods. In addition, as a general video matching algorithm, CVC is also evaluated in traditional video face recognition task on a standard Internet database, i.e., YouTube Celebrities, showing its quite promising performance by using an extremely compact code with only 128 bits.

Fast cloud parameter retrievals of MIPAS/Envisat

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R. Spang

2012-08-01

Full Text Available The infrared limb spectra of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS on board the Envisat satellite include detailed information on tropospheric clouds and polar stratospheric clouds (PSC. However, no consolidated cloud product is available for the scientific community. Here we describe a fast prototype processor for cloud parameter retrieval from MIPAS (MIPclouds. Retrieval of parameters such as cloud top height, temperature, and extinction are implemented, as well as retrieval of microphysical parameters, e.g. effective radius and the integrated quantities over the limb path (surface area density and volume density. MIPclouds classifies clouds as either liquid or ice cloud in the upper troposphere and polar stratospheric clouds types in the stratosphere based on statistical combinations of colour ratios and brightness temperature differences.

Comparison of limb measurements of clouds with model results or cloud parameters from nadir looking instruments is often difficult due to different observation geometries. We therefore introduce a new concept, the limb-integrated surface area density path (ADP. By means of validation and radiative transfer calculations of realistic 2-D cloud fields as input for a blind test retrieval (BTR, we demonstrate that ADP is an extremely valuable parameter for future comparison with model data of ice water content, when applying limb integration (ray tracing through the model fields. In addition, ADP is used for a more objective definition of detection thresholds of the applied detection methods. Based on BTR, a detection threshold of ADP = 10⁷ μm² cm⁻² and an ice water content of 10⁻⁵ g m⁻³ is estimated, depending on the horizontal and vertical extent of the cloud.

Intensive validation of the cloud detection methods shows that the limb-sounding MIPAS instrument has a sensitivity in detecting stratospheric

Retrieval and analysis of atmospheric XCO2 using ground-based spectral observation.

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Qin, Xiu-Chun; Lei, Li-Ping; Kawasaki, Masahiro; Masafumi, Ohashi; Takahiro, Kuroki; Zeng, Zhao-Cheng; Zhang, Bing

2014-07-01

Atmospheric CO2 column concentration (column-averaged dry air mole fractions of atmospheric carbon dioxide) data obtained by ground-based hyperspectral observation is an important source of data for the verification and improvement of the results of CO2 retrieval based on satellite hyperspectral observation. However, few studies have been conducted on atmospheric CO2 column concentration retrieval based on ground-based spectral hyperspectral observation in China. In the present study, we carried out the ground-based hyperspectral observation in Xilingol Grassland, Inner Mongolia of China by using an observation system which is consisted of an optical spectral analyzer, a sun tracker, and some other elements. The atmospheric CO2 column concentration was retrieved using the observed hyperspectral data. The effect of a wavelength shift of the observation spectra and the meteorological parameters on the retrieval precision of the atmospheric CO2 concentration was evaluated and analyzed. The results show that the mean value of atmospheric CO2 concentration was 390.9 microg x mL(-1) in the study area during the observing period from July to September. The shift of wavelength in the range between -0.012 and 0.042 nm will generally lead to 1 microg x mL(-1) deviation in the CO2 retrievals. This study also revealed that the spectral transmittance was sensitive to meteorological parameters in the wavelength range of 6 357-6 358, 6 360-6 361, and 6 363-6 364 cm(-1). By comparing the CO2 retrievals derived from the meteorological parameters observed in synchronous and non-synchronous time, respectively, with the spectral observation, it was showed that the concentration deviation caused by using the non-synchronously observed meteorological parameters is ranged from 0.11 to 4 microg x mL(-1). These results can be used as references for the further improvement of retrieving CO2 column concentration based on spectral observation.

Estimating chlorophyll content of spartina alterniflora at leaf level using hyper-spectral data

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Wang, Jiapeng; Shi, Runhe; Liu, Pudong; Zhang, Chao; Chen, Maosi

2017-09-01

Spartina alterniflora, one of most successful invasive species in the world, was firstly introduced to China in 1979 to accelerate sedimentation and land formation via so-called "ecological engineering", and it is now widely distributed in coastal saltmarshes in China. A key question is how to retrieve chlorophyll content to reflect growth status, which has important implication of potential invasiveness. In this work, an estimation model of chlorophyll content of S. alterniflora was developed based on hyper-spectral data in the Dongtan Wetland, Yangtze Estuary, China. The spectral reflectance of S. alterniflora leaves and their corresponding chlorophyll contents were measured, and then the correlation analysis and regression (i.e., linear, logarithmic, quadratic, power and exponential regression) method were established. The spectral reflectance was transformed and the feature parameters (i.e., "san bian", "lv feng" and "hong gu") were extracted to retrieve the chlorophyll content of S. alterniflora . The results showed that these parameters had a large correlation coefficient with chlorophyll content. On the basis of the correlation coefficient, mathematical models were established, and the models of power and exponential based on SDb had the least RMSE and larger R2 , which had a good performance regarding the inversion of chlorophyll content of S. alterniflora.

The semantic representation of event information depends on the cue modality: an instance of meaning-based retrieval.

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Karlsson, Kristina; Sikström, Sverker; Willander, Johan

2013-01-01

The semantic content, or the meaning, is the essence of autobiographical memories. In comparison to previous research, which has mainly focused on the phenomenological experience and the age distribution of retrieved events, the present study provides a novel view on the retrieval of event information by quantifying the information as semantic representations. We investigated the semantic representation of sensory cued autobiographical events and studied the modality hierarchy within the multimodal retrieval cues. The experiment comprised a cued recall task, where the participants were presented with visual, auditory, olfactory or multimodal retrieval cues and asked to recall autobiographical events. The results indicated that the three different unimodal retrieval cues generate significantly different semantic representations. Further, the auditory and the visual modalities contributed the most to the semantic representation of the multimodally retrieved events. Finally, the semantic representation of the multimodal condition could be described as a combination of the three unimodal conditions. In conclusion, these results suggest that the meaning of the retrieved event information depends on the modality of the retrieval cues.

The semantic representation of event information depends on the cue modality: an instance of meaning-based retrieval.

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Kristina Karlsson

Full Text Available The semantic content, or the meaning, is the essence of autobiographical memories. In comparison to previous research, which has mainly focused on the phenomenological experience and the age distribution of retrieved events, the present study provides a novel view on the retrieval of event information by quantifying the information as semantic representations. We investigated the semantic representation of sensory cued autobiographical events and studied the modality hierarchy within the multimodal retrieval cues. The experiment comprised a cued recall task, where the participants were presented with visual, auditory, olfactory or multimodal retrieval cues and asked to recall autobiographical events. The results indicated that the three different unimodal retrieval cues generate significantly different semantic representations. Further, the auditory and the visual modalities contributed the most to the semantic representation of the multimodally retrieved events. Finally, the semantic representation of the multimodal condition could be described as a combination of the three unimodal conditions. In conclusion, these results suggest that the meaning of the retrieved event information depends on the modality of the retrieval cues.

Global retrieval of soil moisture and vegetation properties using data-driven methods

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Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Kerr, Yann

2017-04-01

Data-driven methods such as neural networks (NNs) are a powerful tool to retrieve soil moisture from multi-wavelength remote sensing observations at global scale. In this presentation we will review a number of recent results regarding the retrieval of soil moisture with the Soil Moisture and Ocean Salinity (SMOS) satellite, either using SMOS brightness temperatures as input data for the retrieval or using SMOS soil moisture retrievals as reference dataset for the training. The presentation will discuss several possibilities for both the input datasets and the datasets to be used as reference for the supervised learning phase. Regarding the input datasets, it will be shown that NNs take advantage of the synergy of SMOS data and data from other sensors such as the Advanced Scatterometer (ASCAT, active microwaves) and MODIS (visible and infra red). NNs have also been successfully used to construct long time series of soil moisture from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and SMOS. A NN with input data from ASMR-E observations and SMOS soil moisture as reference for the training was used to construct a dataset sharing a similar climatology and without a significant bias with respect to SMOS soil moisture. Regarding the reference data to train the data-driven retrievals, we will show different possibilities depending on the application. Using actual in situ measurements is challenging at global scale due to the scarce distribution of sensors. In contrast, in situ measurements have been successfully used to retrieve SM at continental scale in North America, where the density of in situ measurement stations is high. Using global land surface models to train the NN constitute an interesting alternative to implement new remote sensing surface datasets. In addition, these datasets can be used to perform data assimilation into the model used as reference for the training. This approach has recently been tested at the European Centre

Retrieving handwriting by combining word spotting and manifold ranking

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Peña Saldarriaga, Sebastián; Morin, Emmanuel; Viard-Gaudin, Christian

2012-01-01

Online handwritten data, produced with Tablet PCs or digital pens, consists in a sequence of points (x, y). As the amount of data available in this form increases, algorithms for retrieval of online data are needed. Word spotting is a common approach used for the retrieval of handwriting. However, from an information retrieval (IR) perspective, word spotting is a primitive keyword based matching and retrieval strategy. We propose a framework for handwriting retrieval where an arbitrary word spotting method is used, and then a manifold ranking algorithm is applied on the initial retrieval scores. Experimental results on a database of more than 2,000 handwritten newswires show that our method can improve the performances of a state-of-the-art word spotting system by more than 10%.

Evaluation of radar reflectivity factor simulations of ice crystal populations from in situ observations for the retrieval of condensed water content in tropical mesoscale convective systems

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E. Fontaine

2017-06-01

Full Text Available This study presents the evaluation of a technique to estimate cloud condensed water content (CWC in tropical convection from airborne cloud radar reflectivity factors at 94 GHz and in situ measurements of particle size distributions (PSDs and aspect ratios of ice crystal populations. The approach is to calculate from each 5 s mean PSD and flight-level reflectivity the variability of all possible solutions of m(D relationships fulfilling the condition that the simulated radar reflectivity factor (T-matrix method matches the measured radar reflectivity factor. For the reflectivity simulations, ice crystals were approximated as oblate spheroids, without using a priori assumptions on the mass–size relationship of ice crystals. The CWC calculations demonstrate that individual CWC values are in the range ±32 % of the retrieved average CWC value over all CWC solutions for the chosen 5 s time intervals. In addition, during the airborne field campaign performed out of Darwin in 2014, as part of the international High Altitude Ice Crystals/High Ice Water Content (HAIC/HIWC projects, CWCs were measured independently with the new IKP-2 (isokinetic evaporator probe instrument along with simultaneous particle imagery and radar reflectivity. Retrieved CWCs from the T-matrix radar reflectivity simulations are on average 16 % higher than the direct CWCIKP measurements. The differences between the CWCIKP and averaged retrieved CWCs are found to be primarily a function of the total number concentration of ice crystals. Consequently, a correction term is applied (as a function of total number concentration that significantly improves the retrieved CWC. After correction, the retrieved CWCs have a median relative error with respect to measured values of only −1 %. Uncertainties in the measurements of total concentration of hydrometeors are investigated in order to calculate their contribution to the relative error of calculated CWC with respect to

XPIR : Private Information Retrieval for Everyone

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Aguilar-Melchor Carlos

2016-04-01

Full Text Available A Private Information Retrieval (PIR scheme is a protocol in which a user retrieves a record from a database while hiding which from the database administrators. PIR can be achieved using mutuallydistrustful replicated databases, trusted hardware, or cryptography. In this paper we focus on the later setting which is known as single-database computationally- Private Information Retrieval (cPIR. Classic cPIR protocols require that the database server executes an algorithm over all the database content at very low speeds which impairs their usage. In [1], given certain assumptions, realistic at the time, Sion and Carbunar showed that cPIR schemes were not practical and most likely would never be. To this day, this conclusion is widely accepted by researchers and practitioners. Using the paradigm shift introduced by lattice-based cryptography, we show that the conclusion of Sion and Carbunar is not valid anymore: cPIR is of practical value. This is achieved without compromising security, using standard crytosystems, and conservative parameter choices.

Optimizing spatial and temporal constraints for cropland canopy water content retrieval through coupled radiative transfer model inversion

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Boren, E. J.; Boschetti, L.; Johnson, D.

2017-12-01

Water plays a critical role in all plant physiological processes, including transpiration, photosynthesis, nutrient transportation, and maintenance of proper plant cell functions. Deficits in water content cause drought-induced stress conditions, such as constrained plant growth and cellular metabolism, while overabundance of water cause anoxic conditions which limit plant physiological processes and promote disease. Vegetation water content maps can provide agricultural producers key knowledge for improving production capacity and resiliency in agricultural systems while facilitating the ability to pinpoint, monitor, and resolve water scarcity issues. Radiative transfer model (RTM) inversion has been successfully applied to remotely sensed data to retrieve biophysical and canopy parameter estimates, including water content. The successful launch of the Landsat 8 Operational Land Imager (OLI) in 2012, Sentinel 2A Multispectral Instrument (MSI) in 2015, followed by Sentinel 2B in 2017, the systematic acquisition schedule and free data distribution policy provide the opportunity for water content estimation at a spatial and temporal scale that can meet the demands of potential operational users: combined, these polar-orbiting systems provide 10 m to 30 m multi-spectral global coverage up to every 3 days. The goal of the present research is to prototype the generation of a cropland canopy water content product, obtained from the newly developed Landsat 8 and Sentinel 2 atmospherically corrected HLS product, through the inversion of the leaf and canopy model PROSAIL5B. We assess the impact of a novel spatial and temporal stratification, where some parameters of the model are constrained by crop type and phenological phase, based on ancillary biophysical data, collected from various crop species grown in a controlled setting and under different water stress conditions. Canopy-level data, collected coincidently with satellite overpasses during four summer field campaigns

Modelamiento de un sistema de recuperación de imágenes de recursos acuáticos, basado en contenido y calidad de la información Modeling of an aquatic resource image retrieval system, based on content and information quality

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Bell Manrique Losada

2008-07-01

Full Text Available En el marco de los Sistemas de Recuperación de Información, se ubican los Sistemas de Recuperación de Imágenes, los cuales permiten generar procesos de búsqueda y almacenamiento de recursos por medio de coincidencias por palabras claves u otros métodos en tiempo real. Este tipo de sistemas pueden usar como í ndices, contenido visual de las imágenes como color, textura y brillo, y además combinar diferentes atributos para mejorar los procesos de clasificación y relevancia de los resultados del proceso de búsqueda, que se conocen como 'de la calidad de la información'. Este artículo presenta el modelamiento de un Sistema de Recuperación de Imágenes que combina atributos de la recuperación basada en contenido como color, textura y forma, con la recuperación basada en la calidad de la información, como frecuencia de actualización, portabilidad y relevancia, en el contexto de la Colección Digital de Imágenes de Ecosistemas Acuáticos Amazónicos del Grupo CAPREA de la Universidad de la Amazonia.In Information Retrieval Systems, Image Retrieval Systems are located; they allow generating searching and storing processes of resources through coincidences by key words or other real-time methods. This type of systems can use visual content of images (color, texture, and brightness as indices. In addition, these systems combine different attributes to improve the processes of classification and relevance of the searching process results known as 'Information Quality.' This paper presents the modeling of a Image Retrieval System which combines attributes of the contentbased retrieval such as color, texture, and shape, with the Information Quality-based retrieval as updating portability and relevance frequency in the context of Digital Image Collection of Aquatic Amazon Ecosystems of Universidad del Amazonia CAPREA Research Group.

Identification of Case Content with Quantitative Network Analysis

DEFF Research Database (Denmark)

Christensen, Martin Lolle; Olsen, Henrik Palmer; Tarissan, Fabian

2016-01-01

the relevant articles. In order to enhance information retrieval about case content, without relying on manual labor and subjective judgment, we propose in this paper a quantitative method that gives a better indication of case content in terms of which articles a given case is more closely associated with...

Space-based passive microwave soil moisture retrievals and the correction for a dynamic open water fraction

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B. T. Gouweleeuw

2012-06-01

Full Text Available The large observation footprint of low-frequency satellite microwave emissions complicates the interpretation of near-surface soil moisture retrievals. While the effect of sub-footprint lateral heterogeneity is relatively limited under unsaturated conditions, open water bodies (if not accounted for cause a strong positive bias in the satellite-derived soil moisture retrieval. This bias is generally assumed static and associated with large, continental lakes and coastal areas. Temporal changes in the extent of smaller water bodies as small as a few percent of the sensor footprint size, however, can cause significant and dynamic biases. We analysed the influence of such small open water bodies on near-surface soil moisture products derived from actual (non-synthetic data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E for three areas in Oklahoma, USA. Differences between on-ground observations, model estimates and AMSR-E retrievals were related to dynamic estimates of open water fraction, one retrieved from a global daily record based on higher frequency AMSR-E data, a second derived from the Moderate Resolution Imaging Spectroradiometer (MODIS and a third through inversion of the radiative transfer model, used to retrieve soil moisture. The comparison demonstrates the presence of relatively small areas (<0.05 of open water in or near the sensor footprint, possibly in combination with increased, below-critical vegetation density conditions (optical density <0.8, which contribute to seasonally varying biases in excess of 0.2 (m³ m⁻³ soil water content. These errors need to be addressed, either through elimination or accurate characterisation, if the soil moisture retrievals are to be used effectively in a data assimilation scheme.

User-Centric Multi-Criteria Information Retrieval

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Wolfe, Shawn R.; Zhang, Yi

2009-01-01

Information retrieval models usually represent content only, and not other considerations, such as authority, cost, and recency. How could multiple criteria be utilized in information retrieval, and how would it affect the results? In our experiments, using multiple user-centric criteria always produced better results than a single criteria.

Content-based video indexing and searching with wavelet transformation

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Stumpf, Florian; Al-Jawad, Naseer; Du, Hongbo; Jassim, Sabah

2006-05-01

Biometric databases form an essential tool in the fight against international terrorism, organised crime and fraud. Various government and law enforcement agencies have their own biometric databases consisting of combination of fingerprints, Iris codes, face images/videos and speech records for an increasing number of persons. In many cases personal data linked to biometric records are incomplete and/or inaccurate. Besides, biometric data in different databases for the same individual may be recorded with different personal details. Following the recent terrorist atrocities, law enforcing agencies collaborate more than before and have greater reliance on database sharing. In such an environment, reliable biometric-based identification must not only determine who you are but also who else you are. In this paper we propose a compact content-based video signature and indexing scheme that can facilitate retrieval of multiple records in face biometric databases that belong to the same person even if their associated personal data are inconsistent. We shall assess the performance of our system using a benchmark audio visual face biometric database that has multiple videos for each subject but with different identity claims. We shall demonstrate that retrieval of relatively small number of videos that are nearest, in terms of the proposed index, to any video in the database results in significant proportion of that individual biometric data.

Technical Note: Regularization performances with the error consistency method in the case of retrieved atmospheric profiles

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S. Ceccherini

2007-01-01

Full Text Available The retrieval of concentration vertical profiles of atmospheric constituents from spectroscopic measurements is often an ill-conditioned problem and regularization methods are frequently used to improve its stability. Recently a new method, that provides a good compromise between precision and vertical resolution, was proposed to determine analytically the value of the regularization parameter. This method is applied for the first time to real measurements with its implementation in the operational retrieval code of the satellite limb-emission measurements of the MIPAS instrument and its performances are quantitatively analyzed. The adopted regularization improves the stability of the retrieval providing smooth profiles without major degradation of the vertical resolution. In the analyzed measurements the retrieval procedure provides a vertical resolution that, in the troposphere and low stratosphere, is smaller than the vertical field of view of the instrument.