Developments in multimedia technologies have paved way for the storage of huge collections of video doc- uments on computer systems. It is essential to design tools for content-based access to the documents, so as to ...Developments in multimedia technologies have paved way for the storage of huge collections of video doc- uments on computer systems. It is essential to design tools for content-based access to the documents, so as to allow an efficient exploitation of these collections. Content based anal- ysis provides a flexible and powerful way to access video data when compared with the other traditional video analysis tech- niques. The area of content based video indexing and retrieval （CBVIR）, focusing on automating the indexing, retrieval and management of video, has attracted extensive research in the last decade. CBVIR is a lively area of research with endur- ing acknowledgments from several domains. Herein a vital assessment of contemporary researches associated with the content-based indexing and retrieval of visual information. In this paper, we present an extensive review of significant researches on CBV1R. Concise description of content based video analysis along with the techniques associated with the content based video indexing and retrieval is presented.展开更多
As commercial motion capture systems are widely used, more and more 3D motion libraries become available, reinforcing the demand for efficient indexing and retrieving methods. Usually, the user will only have a sketch...As commercial motion capture systems are widely used, more and more 3D motion libraries become available, reinforcing the demand for efficient indexing and retrieving methods. Usually, the user will only have a sketchy idea of which kind of motion to look for in the motion database. As a result, how to clearly describe the user’s demands is a bottleneck for motion retrieval system. This paper presented a framework that can handle this problem effectively for motion retrieval. This content-based retrieval system supports two kinds of query modes: textual query mode and query-by-example mode. In both query modes, user’s input is translated into scene description language first, which can be processed by the system efficiently. By using various kinds of qualitative features and adaptive segments of motion capture data stream, indexing and retrieval methods are carried out at the segment level rather than at the frame level, making them quite efficient. Some experimental examples are given to demonstrate the effectiveness and efficiency of the proposed algorithms.展开更多
文摘Developments in multimedia technologies have paved way for the storage of huge collections of video doc- uments on computer systems. It is essential to design tools for content-based access to the documents, so as to allow an efficient exploitation of these collections. Content based anal- ysis provides a flexible and powerful way to access video data when compared with the other traditional video analysis tech- niques. The area of content based video indexing and retrieval （CBVIR）, focusing on automating the indexing, retrieval and management of video, has attracted extensive research in the last decade. CBVIR is a lively area of research with endur- ing acknowledgments from several domains. Herein a vital assessment of contemporary researches associated with the content-based indexing and retrieval of visual information. In this paper, we present an extensive review of significant researches on CBV1R. Concise description of content based video analysis along with the techniques associated with the content based video indexing and retrieval is presented.
基金National Natural Science Foundation of Chi-na (No. 60573147, No. 60373070) Mi-crosoft Research Asia (Project-2004-Image-01)
文摘As commercial motion capture systems are widely used, more and more 3D motion libraries become available, reinforcing the demand for efficient indexing and retrieving methods. Usually, the user will only have a sketchy idea of which kind of motion to look for in the motion database. As a result, how to clearly describe the user’s demands is a bottleneck for motion retrieval system. This paper presented a framework that can handle this problem effectively for motion retrieval. This content-based retrieval system supports two kinds of query modes: textual query mode and query-by-example mode. In both query modes, user’s input is translated into scene description language first, which can be processed by the system efficiently. By using various kinds of qualitative features and adaptive segments of motion capture data stream, indexing and retrieval methods are carried out at the segment level rather than at the frame level, making them quite efficient. Some experimental examples are given to demonstrate the effectiveness and efficiency of the proposed algorithms.
