In this paper, we present an approach to clustering video sequences and images for efficient retrieval using relative entropy as our cost criterion. In addition, our experiments indicate that relative entropy is a goo...
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In this paper, we present an approach to clustering video sequences and images for efficient retrieval using relative entropy as our cost criterion. In addition, our experiments indicate that relative entropy is a good similarity measure for content-based retrieval. In our clustering work, we treat images and video as probability density functions over the extracted features. This leads us to formulate a general algorithm for clustering densities. In this context, it can be seen that an euclidean distance between features and the Kullback-Liebler (KL) divergence give equivalent clustering. In addition, the asymmetry of the KL divergence leads to another clustering. Our experiments indicate that this clustering is more robust to noise and distortions compared with the one resulting from euclidean norm.
A different approach to content-based retrieval and a novel framework for classification of visual information are proposed. The Visual Apprentice which is an implementation of the framework for still images and video...
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A different approach to content-based retrieval and a novel framework for classification of visual information are proposed. The Visual Apprentice which is an implementation of the framework for still images and video that uses a combination of lazy-learning, decision trees, and evolution programs for classification and grouping is introduced. Examples and results are given to demonstrate the applicability of the proposed approach to perform visual classification and detection.
A prototype of the content-based imageretrieval system is implemented based on the algorithms introduced in this paper. The image contents at the high levels are extracted. The fuzzy C-means classifier is employed to...
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A prototype of the content-based imageretrieval system is implemented based on the algorithms introduced in this paper. The image contents at the high levels are extracted. The fuzzy C-means classifier is employed to compute the object clusters and provide useful information for overlapped clusters. The automatic image segmentation and categorisation is achieved. To obtain the context for imageretrieval, the subjective context and the objective context are modelled by means of the fuzzy sets theory. The system is able to trace the users' interactions during retrieval. The refinements of the retrieval results can be made while the users are submitting the queries telling the specific requirements.
The Web-based Medical Information retrieval System (WebMIRS) allows Internet access to databases containing 17,000 digitized x-ray spine images and associated text data from National Health and Nutrition Examinations ...
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The Web-based Medical Information retrieval System (WebMIRS) allows Internet access to databases containing 17,000 digitized x-ray spine images and associated text data from National Health and Nutrition Examinations Surveys (NHANES). WebMIRS allows SQL query of the text, and viewing of the returned text records and images using a standard browser. We are now working (1) to determine utility of data directly derived from the images in our databases and (2) to investigate the feasibility of computer-assisted or automated indexing of the images to support imageretrieval of images of interest to biomedical researchers in the field of osteoarthritis. To build an initial database based on image data, we are manually segmenting a subset of the vertebrae, using techniques from vertebral morphometry. From this, we will derive and add to the database vertebral features. This image-derived data will enhance the user's data access capability by enabling the creation of combined SQL/image-content queries.
video parsing is an important step in content-based indexing techniques where the input video is decomposed into segments with uniform content. In video parsing detection of scene changes is one of the approaches wide...
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video parsing is an important step in content-based indexing techniques where the input video is decomposed into segments with uniform content. In video parsing detection of scene changes is one of the approaches widely used for extracting key frames from the video sequence. In this paper, an algorithm based on motion vectors is proposed to detect sudden scene changes and gradual scene changes (camera movements such as panning, tilting and zooming). Unlike some of the existing schemes, the proposed scheme is capable of detecting both sudden and gradual changes in uncompressed as well as compressed domain video. It is shown that the resultant motion vector can be used to identify and classify gradual changes due to camera movements. Results show that algorithm performed as well as the histogram-based schemes with uncompressed video. The performance of the algorithm was also investigated with H.263 compressed video. The detection and classification of both sudden and gradual scene changes was successfully demonstrated.
Content based retrieval on large multimedia database attracts the interests of many researchers, but the database architecture needed for content based retrieval is still an open problem. Traditional relation database...
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Content based retrieval on large multimedia database attracts the interests of many researchers, but the database architecture needed for content based retrieval is still an open problem. Traditional relation database system does not support the high-dimension feature form content description and indexing, thus is limited in its content based retrieval function. Some systems do support high-dimension feature form content description and indexing, but lacks descriptions and query expressions on media object content and relations. In this paper, we present our study results on query mechanism and proposed CbExpr - a powerful flexible query expression mechanism on media object. Based on CbExpr we proposed GMA (general mediabase architecture) - a general architecture for management and content based retrieval on large media databases, and videoBase - a content based videoretrieval system is present as example of GMA. Basic thoughts, considerations, and definitions are presented in the paper, also with some implementation details.
Organizing video shots into hierarchy structures is very important for efficient browsing and retrieval on large videodatabases, and many shot organizing methods have been proposed. Most algorithms are based on autom...
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Organizing video shots into hierarchy structures is very important for efficient browsing and retrieval on large videodatabases, and many shot organizing methods have been proposed. Most algorithms are based on automatic clustering schemes, which usually fail to give satisfactory results in real applications. In this paper, we proposed a preprocessing technology for interactive shot organizing - similarity sequence. It differs from traditional shot organizing methods in that it does not classify shots, instead it only reorders the shot sequence so that similar shots appear near each other, thus provides an effective interactive shot organizing interface and leaves the classification work to the user. A measure called similarity length was introduced to evaluate the similarity between adjacent shots in shot sequence, and an improved genetic algorithm was developed to calculate the similarity sequence. Basic thoughts and implementation details are provided, also with experiment results on real videos and analysis.
With the abstraction of digital video as the corresponding binary video- a process which upon numerous subjective experimentation seems to preserve (most of the) intelligibility of video content- we can pursue a preci...
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With the abstraction of digital video as the corresponding binary video- a process which upon numerous subjective experimentation seems to preserve (most of the) intelligibility of video content- we can pursue a precise and analytic approach to (digital videostorage and retrieval) algorithm design that are based upon geometrical (morphological) intuition. The foremost and tangible general benefit of such abstraction, however, is the immediate reductions of both data and computational complexities involved in implementing various algorithms and databases. The general paradigm presented may be utilized to address all issues pertaining to video library construction including visualization, optimum feedback query generation, object recognition, e.t.c., but the primary focus of attention in this paper are the ones pertaining to detection of fast (including presence of flashlights) and gradual scene changes (such as dissolves, fades, and various special effects such as wipes). Upon simulation we observed that we can achieve performances comparable to those of others with drastic reductions in both storage and computational complexities. Furthermore, since the conversion from grayscale to binary videos can be performed directly (with minimal additional computation) in the compressed domain by thresholding on the DCT DC coefficients themselves (or by using the contour information attached to MPEG4 formats), the algorithms presented herein are ideally suited for performing fast (on-the-fly) determinations of scene change, object recognition and/or tracking, and other more intelligent tasks traditionally requiring heavy demand on computational and/or storage complexities. The fast determinations may then be used on their own merits or can be used in conjunction or complementation with other higher-layer information in the future.
The color hologram of an image has been widely used as a feature descriptor for the image in content-based retrieval applications. In this paper, some results from our investigation efforts into to usage are reported....
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The color hologram of an image has been widely used as a feature descriptor for the image in content-based retrieval applications. In this paper, some results from our investigation efforts into to usage are reported. We outline three typical color space quantization schemes used in our experiments and introduce the soft-decision histogramming method to eliminate the discontinuity problem in traditional color histogram population process. Then, to improve the effectiveness of color histogram based retrieval algorithms, several similarity metrics are proposed for comparing color histograms, including three special forms of the Kantorovich metric.
We present a fast algorithm for computing the singular value decomposition (SVD) of a matrix consisting of the frames from a video sequence. The computational efficiency of this algorithm derives from the observation ...
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We present a fast algorithm for computing the singular value decomposition (SVD) of a matrix consisting of the frames from a video sequence. The computational efficiency of this algorithm derives from the observation that portions of a video sequence will consist of sets of correlated frames. We then show that the information obtained from the SVD can be used to analyze video sequences to obtain information such as scene breaks, scene query, reduced-order shot representation and key frame determination. We illustrate this approach on several video sequences.
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