A novel algorithm for imageretrieval is presented. The basic idea of the new algorithm is that the constituent segments of the images are used to retrieve images within a digital library. imageretrieval using segmen...
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A novel algorithm for imageretrieval is presented. The basic idea of the new algorithm is that the constituent segments of the images are used to retrieve images within a digital library. imageretrieval using segments is distinctive in that the local features of the image are used to retrieve the image instead of the typically utilized global features. In our algorithm, the given image is first segmented into dominant components and then the features of these components are extracted to perform retrieval. The features corresponding to each component are used to calculate the distance between components in the matching process. Each image is ranked based on the component wise distance measure with respect to the query component. One of the advantages of the algorithm is that, for a given retrievalimage, the user can select a query segment with which to perform retrieval, thus it can satisfy different needs from different users.
With the rapid growth of multimedia information in forms of digital image and video libraries, there is an increasing need for intelligent database management tools with an efficient information retrieval system. For ...
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With the rapid growth of multimedia information in forms of digital image and video libraries, there is an increasing need for intelligent database management tools with an efficient information retrieval system. For this purpose, we propose a hierarchical retrieval system where shape, color and motion characteristics of human body are captured in compressed and uncompressed domains. The proposed retrieval method provides human detection and activity recognition at different resolution levels from low complexity to low false rates and connects low level features to high level semantics by developing relational object and activity presentations. The available information of standard video compression algorithms are used in order to reduce the amount of time and storage needed for the information retrieval. The principal component analysis is used for activity recognition using MPEG motion vectors and results are presented for walking, kicking, and running to demonstrate that the classification among activities is clearly visible. For low resolution and monochrome images it is demonstrated that the structural information of human silhouettes can be captured from AC-DCT coefficients. The system performance is tested on 40 images that contain a total of 126 nonoccluded frontal poses and the algorithm can detect 101 of them correctly. The finest details in the images and video sequences are obtained from the uncompressed domain via model based segmentation and graph matching for an in depth analysis of human bodies. The detection rate for human body parts is 70.27% for images and sequences including human body regions at different resolutions and with different postures.
The proceeding contains 41 papers from the conference on storage and retrieval for Media databases 2002. The topics discussed include: structural segmentation for multimedia content-based information retrieval;seeded ...
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The proceeding contains 41 papers from the conference on storage and retrieval for Media databases 2002. The topics discussed include: structural segmentation for multimedia content-based information retrieval;seeded image segmentation for content-based imageretrieval application;automatic classification of images on the Web;novel imageretrieval technique using salient edges;extensible feature management engine for imageretrieval;search and retrieval of imagedatabases;video segmentation;video indexing and video processing.
作者:
Luo, MBai, XSXu, GYTsinghua Univ
Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China
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 inhibitor...
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ISBN:
(纸本)0819444162
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.
We propose an eminent scene change detection (SCD) algorithm, and also present a new video-indexing method for fast content-based browsing and retrieval in videodatabases. The first step in our approach is to extract...
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We propose an eminent scene change detection (SCD) algorithm, and also present a new video-indexing method for fast content-based browsing and retrieval in videodatabases. The first step in our approach is to extract representative frames by using the proposed SCD algorithm from the Motion Picture Experts Group (MPEG) video sequence. Thereafter, we perform the video indexing by applying the rosette pattern (RP) to the extracted representative frames, and retrieve them. A principal cause of these processing procedures is that the performance of video indexing and retrieval system depends on the SCD. And how accurately SCD performs as well as a preprocessing are some of the considerations that we must take into account. Therefore, we classify every possible scene change that may happen within the video into three types: abrupt, gradual, and flashlight. And we detect every kind of scene change. Our SCD algorithm is better than the conventional ones in terms of SCD performance and we demonstrate its superiority by experiments. We also extract representative frames that represent series of detected scenes by the proposed accumulative histogram intersection measure (AHIM). Finally, we apply the RP to extracted representative frames for indexing, we can remarkably reduce the number of pixels (NOP) required to index and excellently retrieve the video scene. (C) 2002 Society of Photo-Optical Instrumentation Engineers.
The maintenance required on modern media databases to allow efficient indexing and retrieval is becoming an ever-increasing burden. An explosion in the content of the internet [1], the lowering in costs associated wit...
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ISBN:
(纸本)0780373006
The maintenance required on modern media databases to allow efficient indexing and retrieval is becoming an ever-increasing burden. An explosion in the content of the internet [1], the lowering in costs associated with imaging devices and the digital revolution have placed much higher requirements on the storage of graphics and video, both in commercial domains and at home. Fully automated indexing systems will be very desirable to manage and organise these large amounts of data. We tackle the problem of image indexing in the compressed domain using a Neural Network extraction technique together with a DCT domain watermark. This approach allows the indexing of the image entirely in the DCT domain, thus benefits from substantial computational savings. Coupled with DCT based watermarking, the technique allows for the real-time embedding of indexing data in popular image (JPEG) and video (MPEG) formats. Such a low-cost combination (both in processing and key storage) makes the method highly suitable for the use in portable imaging devices such as digital cameras were it would be highly desirable to have some means of indexing the images they have stored.
In this paper we investigate the distribution of shot lengths for video sequences containing diverse content. Accurate models for shot lengths are important to model video both for content-based retrieval applications...
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In this paper we investigate the distribution of shot lengths for video sequences containing diverse content. Accurate models for shot lengths are important to model video both for content-based retrieval applications and for performing queuing analysis for the design of video buffers in multimedia networks. Using a large dataset collected from CSPAN programs we have analyze the Pareto, Weibull, and gamma distributions as possible models for the shot length distribution. We have compare the goodness of fit of these possible distribution models using the Kolmogorov-Smirnov statistic.
Due to the rapidly growing multimedia content available on the internet it is highly desirable to index multimedia data automatically and to provide content based search and retrieval functionalities. The first step i...
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Due to the rapidly growing multimedia content available on the internet it is highly desirable to index multimedia data automatically and to provide content based search and retrieval functionalities. The first step in order to describe and annotate video data is to split the sequences into sub-shots which are related to semantic units. This paper addresses unsupervised scene change detection and keyframe selection of video sequences. Unlike other methods this is performed by using a standardized multimedia content description of the video data. We apply the MPEG-7 scalable color descriptor and the edge histogram descriptor for shot boundary detection and show that this method performs well. Furthermore, we propose to store the output data of our system in a video segment description scheme to provide simple but efficient search and retrieval functionalities for video scenes based on color features.
Temporal segmentation of a video sequence into different shots is fundamental to a number of videoretrieval and analysis applications. Motion estimation has been wildly used in many applications of video processing, ...
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Temporal segmentation of a video sequence into different shots is fundamental to a number of videoretrieval and analysis applications. Motion estimation has been wildly used in many applications of video processing, since it provides the most essential information for an image sequence. In this paper, we explore the possibility to exploit motion and illumination estimation in a video sequence to detect various types of shot changes. Optical flow is the motion vector computed at each pixel in an image sequence from intensity variation. Traditionally, optical flow computational algorithms were derived from the brightness constancy assumption. In this paper, we employ a generalized optical flow constraint that includes an illumination parameter to model local illumination changes. An iterative optical flow and illumination estimation algorithm is developed in this paper to refine the generalized optical flow constraints step by step, thus leading to a very accurate estimation of the optical flow and illumination parameters. Two robust measures are defined from the mean and standard deviation of the estimated intensity compensation values for all the blocks in the same image. Either of these two measures corresponds significantly to various types of shot changes. We show the usefulness of these two measures through experiments.
Due to the increasing amount of information carried by video, video analysis that clips video as changed scenes or key-frames becomes essential for efficient video indexing. In this paper, we proposed a compressed dom...
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Due to the increasing amount of information carried by video, video analysis that clips video as changed scenes or key-frames becomes essential for efficient video indexing. In this paper, we proposed a compressed domain scene change detection and camera motion characterization algorithm. We believe that the most vital inherent information bided in the MPEG bitstream, which can aid scene shot and sub-shot detection, are the motion vector and the macroblock type statistics. We evaluate the results of the scene change detection and camera motion characterization to get the accurate shot and sub-shot location.
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