A new system, the so-called MUVIS, is introduced for content-based indexing and retrieval for image database management systems. In addition to traditional indexing by key words, MUVIS allows indexing of objects and i...
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A new system, the so-called MUVIS, is introduced for content-based indexing and retrieval for image database management systems. In addition to traditional indexing by key words, MUVIS allows indexing of objects and images based on color, texture, shape and objects layout inside them. Due to the use of large vector features, the pyramid trees are employed to create the index structure.
this paper is concerned with estimating a probability density function of human skin color using a finite Gaussian mixture model whose parameters are estimated through the EM algorithm. Hawkins' statistical test o...
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this paper is concerned with estimating a probability density function of human skin color using a finite Gaussian mixture model whose parameters are estimated through the EM algorithm. Hawkins' statistical test on the normality and homoscedasticity (common covariance matrix) of the estimated Gaussian mixture models is performed and McLachlan's bootstrap method is used to test the number of components in a mixture. Experimental results show that the estimated Gaussian mixture model fits skin images from a large database. Applications of the estimated density function in image and videodatabases are presented.
imageretrieval systems that compare the query image exhaustively with each individual image in the database are not scalable to large databases. A scalable search system should ensure that the search time does not in...
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imageretrieval systems that compare the query image exhaustively with each individual image in the database are not scalable to large databases. A scalable search system should ensure that the search time does not increase linearly withthe number of images in the database. We present a clustering based indexing technique, where the images in the database are grouped into clusters of images with similar color content using a hierarchical clustering algorithm. At search time the query image is not compared with all the images in the database, but only with a small subset. Experiments show that this clustering based approach offers a superior response time with a high retrieval accuracy. Experiments with different database sizes indicate that for a given retrieval accuracy the search time does not increase linearly withthe database size.
image histogram is an image feature widely used in content-based imageretrieval and video segmentation. It is simple to compute yet very effective as a feature in detecting image-to-image similarity, or frame-to-fram...
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image histogram is an image feature widely used in content-based imageretrieval and video segmentation. It is simple to compute yet very effective as a feature in detecting image-to-image similarity, or frame-to-frame dissimilarity. While the image histogram captures the global distribution of different intensities or colors well, it does not contain any information about the spatial distribution of pixels. In this paper, we propose to incorporate spatial information into the image histogram by computing features from the spatial distance between pixels belonging to the same intensity or color. In addition to the frequency count of the intensity or color, the mean, variance, and entropy of the distances are computed to form an Augmented image Histogram. Using the new feature, we preformed experiments on a set of color images and a color video sequence. Experimental results demonstrate that the Augmented image Histogram performs significantly better than the conventional color histogram, both in imageretrieval and video shot segmentation.
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 withthe one resulting from euclidean norm.
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.
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.
A simultaneous learning and indexing technique is proposed for efficient content-based retrieval of images that can be described by feature vectors. this technique builds a compact high-dimensional index while taking ...
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A simultaneous learning and indexing technique is proposed for efficient content-based retrieval of images that can be described by feature vectors. this technique builds a compact high-dimensional index while taking into account that the raw feature space needs to be adjusted for each new application. Withthis technique, much better efficiency can be achieved as compared to those techniques that do not make provisions for efficient indexing.
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.
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.
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