video Segmentation plays an integral role in many multimedia applications such as digital libraries, content management systems, and various other video browsing, indexing, and retrieval systems. Many algorithms for s...
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video Segmentation plays an integral role in many multimedia applications such as digital libraries, content management systems, and various other video browsing, indexing, and retrieval systems. Many algorithms for segmentation of video have appeared in the past few years. Most of these algorithms perform well on cuts, but yield poor performance on gradual transitions or special effect edits. A complete video segmentation system must achieve good performance on special effect edit detection also. In this paper, we discuss the performance of our videoTrails based algorithms with other existing special effect edit detection algorithms in literature. Results from experiments testing for the ability to detect edits from TV programs ranging from commercials to news magazine programs, and also diverse special effect edits introduced by us have been shown.
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.
In this paper we address the problem of choosing appropriate features to describe the content of still pictures or video sequences including audio. As the computational analysis of these features is often time-consumi...
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In this paper we address the problem of choosing appropriate features to describe the content of still pictures or video sequences including audio. As the computational analysis of these features is often time-consuming it is useful to identify a minimal set allowing for an automatic classification of some class or genre. Further it can be shown that deleting the coherence of the features characterizing some class is not suitable to guarantee an optimal classification result. the central question of the paper is thus which features should be selected and how they should be weighted to optimize a classification problem.
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 imageretrieval has recently become one of the most active research areas, due to the massive increase in the amount and complexity of digitized data being stored, transmitted and accessed. We present he...
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Content Based imageretrieval has recently become one of the most active research areas, due to the massive increase in the amount and complexity of digitized data being stored, transmitted and accessed. We present here a prototype implementation of DRAWSEARCH, an imageretrieval by content system that uses color and shape (and texture in the near future) features to index and retrieve images. the system, currently being tested and improved, is designed to increase interactivity with users posing queries over the Internet and avails of a Java client for query by sketch. It also implements relevance feedback to allow users dynamically refine queries. Experiments show that the proposed approach can greatly reduce the user's effort to compose a query while capturing his/her information need with greater precision.
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.
this paper introduces a model of spatio-temporal database that we are developing to query interesting events in video sequences. the database that we are designing is pushing the state of the art for a number of field...
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this paper introduces a model of spatio-temporal database that we are developing to query interesting events in video sequences. the database that we are designing is pushing the state of the art for a number of fields, and there are many issues that are still waiting a satisfactory solution. In this paper we present our (albeit still partial) answer to some of these problems, and the future directions of our work. Our design is divided in two layers: a Logbook which operates as a short time repository of unsummarized and unprocessed data, and a long term spatio-temporal database which stores and queries summarized data.
this paper proposes an imageretrieval system which searches a database for images similar to a target imagined by a user. the system uses image features, rather than keywords, and retrieves images by reducing a multi...
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this paper proposes an imageretrieval system which searches a database for images similar to a target imagined by a user. the system uses image features, rather than keywords, and retrieves images by reducing a multidimensional feature space generated by the image feature vectors. First, the system presents the user some sample images with a suitable feature vector value and requires the user's interaction to obtain information on which image is similar to the target he/she has in his/her mind. then, the information is used to appropriately reduce the feature space. this process is continued until the target region is reduced to a suitable volume. Since this method requires neither real target image nor keywords in retrieving, it is quite simple and practical. Experimental results show the advantage and efficiency of the proposed system.
We have developed a TV-on-demand system that provides playback of a television program after a period ranging from a few seconds to one week after broadcast, and have conducted usage trials in cooperation with a telev...
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We have developed a TV-on-demand system that provides playback of a television program after a period ranging from a few seconds to one week after broadcast, and have conducted usage trials in cooperation with a television station in Nagano Prefecture of Japan. this system has been achieved through the development of various technologies such as automatic updating of stored television programs and contents retrieval by telop characters. Users in the trials can begin playback of a television program immediately after its broadcast has begun. the purpose of the trials was to evaluate the system's usability in applications such as contents retrieval, selective viewing of commercials, and customer service at the television station. this paper presents applied technologies and some experimental results and also addresses a new direction of information retrieval system based on the evaluation of the usage trials.
this paper presents algorithms to deal with problems associated with indexing high-dimensional feature vectors that characterize video data. Indexing high dimensional vectors is well known to be computationally expens...
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this paper presents algorithms to deal with problems associated with indexing high-dimensional feature vectors that characterize video data. Indexing high dimensional vectors is well known to be computationally expensive. Our solution is to optimally split the high dimensional vector into a few low dimensional feature vectors and querying the system for each feature vector. this involves solving an important sub-problem: developing a model of retrievalthat enables us to query the system efficiently. Once we formulate the retrieval problem in terms of a retrieval model, we present an optimality criterion to maximize the number of results using this model. the criterion is based on a novel idea of using the underlying probability distribution of the feature vectors. A branch-and-prune strategy optimized per each query, is developed. this uses the set of features derived from the optimality criterion. Our results show that the algorithm performs well, giving a speedup of a factor of 25 with respect to a linear search while retaining the same level of Recall.
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