This paper proposes a novel algorithm for extracting key frames to represent video shots. Re- garding whether, or how well, a key frame represents a shot, different interpretations have been suggested. We develop ou...This paper proposes a novel algorithm for extracting key frames to represent video shots. Re- garding whether, or how well, a key frame represents a shot, different interpretations have been suggested. We develop our algorithm on the assumption that more important content may demand more attention and may last relatively more frames. Unsupervised clustering is used to divide the frames into clusters within a shot, and then a key frame is selected from each candidate cluster. To make the algorithm independent of video sequences, we employ a statistical model to calculate the clustering threshold. The proposed algo- rithm can capture the important yet salient content as the key frame. Its robustness and adaptability are validated by experiments with various kinds of video sequences.展开更多
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
In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture (MoCap) has been widely used in the field of large amount of motion capture data...In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture (MoCap) has been widely used in the field of large amount of motion capture databases are available and this is significant for the reuse of motion data. But due to the high degree of freedoms and high capture frequency, the dimension of the mo- tion capture data is usually very high and this will lead to a low efficiency in data processing. So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can't ignore. In this paper, first we lay out some problems about the key techniques in motion capture data processing. Then the existing approaches are analyzed and sum- marized. At last, some future work is proposed.展开更多
基金Supported by the National Natural Science Foundation of China(No. 60072009)
文摘This paper proposes a novel algorithm for extracting key frames to represent video shots. Re- garding whether, or how well, a key frame represents a shot, different interpretations have been suggested. We develop our algorithm on the assumption that more important content may demand more attention and may last relatively more frames. Unsupervised clustering is used to divide the frames into clusters within a shot, and then a key frame is selected from each candidate cluster. To make the algorithm independent of video sequences, we employ a statistical model to calculate the clustering threshold. The proposed algo- rithm can capture the important yet salient content as the key frame. Its robustness and adaptability are validated by experiments with various kinds of video sequences.
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Supported by the National Natural Science Foundation of China(No.60875046)by Program for Changjiang Scholars and Innovative Research Team in University(No.IRT1109)+5 种基金the Key Project of Chinese Ministry of Education(No.209029)the Program for Liaoning Excellent Talents in University(No.LR201003)the Program for Liaoning Science and Technology Research in University(No.LS2010008,2009S008,2009S009,LS2010179)the Program for Liaoning Innovative Research Team in University(Nos.2009T005,LT2010005,LT2011018)Natural Science Foundation of Liaoning Province(201102008)by"Liaoning BaiQianWan Talents Program(2010921010,2011921009)"
文摘In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture (MoCap) has been widely used in the field of large amount of motion capture databases are available and this is significant for the reuse of motion data. But due to the high degree of freedoms and high capture frequency, the dimension of the mo- tion capture data is usually very high and this will lead to a low efficiency in data processing. So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can't ignore. In this paper, first we lay out some problems about the key techniques in motion capture data processing. Then the existing approaches are analyzed and sum- marized. At last, some future work is proposed.