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 with the 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 with the 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 a multiresolution approach for video indexing and feature matching of subband coded videodatabases. Subband coding refers to a coding technique where the input images are quantized after bein...
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ISBN:
(纸本)0819414808
In this paper we present a multiresolution approach for video indexing and feature matching of subband coded videodatabases. Subband coding refers to a coding technique where the input images are quantized after being decomposed into several narrow spatial frequency bands by filtering and decimation. Five different approaches were tested for scene change detection which is applied only on the lowest subband for computational efficiency. Two kinds of scene changes, abrupt and smoothly accumulated scene changes, mark the beginning of new scene segments. An index for each scene segment is the histogram of two representative frames, which we take to be the first and the last frame of the scene for simplicity. Using the approach of query by example, the index matching algorithm takes a multi-resolution approach by hierarchically comparing histograms at different resolutions. The search algorithm for the match between example query and its target scene segment starts from the coarsest resolution, and moves to the next finer resolution until a single match is obtained or the finest resolution is reached. Experimental results are presented, and the proposed indexing technique appears to be promising for its computational efficiency and its inherent hierarchical search procedure.
The detection of shot boundaries in video sequences is an important task for generating indexed videodatabases. This paper provides a comprehensive quantitative comparison of the metrics that have been applied to sho...
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The detection of shot boundaries in video sequences is an important task for generating indexed videodatabases. This paper provides a comprehensive quantitative comparison of the metrics that have been applied to shot boundary detection. In addition, several standardized statistical tests that have not been applied to this problem, and three new metrics, are considered. A mathematical framework for quantitatively comparing metrics is supplied. Experimental results based on a video database containing 39,000 frames are included.
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.
video contains multiple types of audio and visual information, which are difficult to extract, combine or trade-off in general video information retrieval. This paper provides an evaluation on the effects of different...
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ISBN:
(纸本)0819448214
video contains multiple types of audio and visual information, which are difficult to extract, combine or trade-off in general video information retrieval. This paper provides an evaluation on the effects of different types of information used for videoretrieval from a video collection. A number of different sources of information are present in most typical broadcast video collections and can be exploited for information retrieval. We will discuss the contributions of automatically recognized speech transcripts, image similarity matching, face detection and video OCR in the contexts of experiments performed as part of 2001 TREC videoretrieval Track evaluation performed by the National Institute of Standards and Technology. For the queries used in this evaluation, image matching and video OCR proved to be the deciding aspects of video information retrieval.
This paper describes a novel system for real-time object extraction from a moving videoimage. The object extraction method employed in this system has two features. The first one is multi-channel thresholding in a co...
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ISBN:
(纸本)0819414808
This paper describes a novel system for real-time object extraction from a moving videoimage. The object extraction method employed in this system has two features. The first one is multi-channel thresholding in a color space for extracting a target with several colors as a single object. The color space is normalized by luminance to make the process robust against light intensity fluctuations. The other feature is a key algorithm, called sequential growing, which exploits interfield correlation of a videoimage and realizes field-rate operation. In this algorithm, a binary image representing a target object is generated by growing an initially given mask image horizontally and vertically until whole object is extracted. We have also fabricated a 0.8 micrometers CMOS chip to incorporate the system into a compact video camera, which has successfully extracted objects in a number of general scenes.
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.
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.
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.
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