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	Key frame extraction using unsupervised clustering based on a statistical model
	Yang Shuping ; Lin Xinggang
	2010-05-06 ; 2010-05-06
关键词	Theoretical or Mathematical/ feature extraction image sequences pattern clustering statistical analysis unsupervised learning video signal processing/ video retrieval motion compensation key frame extraction unsupervised clustering statistical model video shots video sequences clustering threshold robustness adaptability/ B6135 Optical, image and video signal processing B0240Z Other topics in statistics C5260D Video signal processing C1140Z Other topics in statistics C1230L Learning in AI
中文摘要	This paper proposes a novel algorithm for extracting key frames to represent video shots. Regarding 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 algorithm 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.
语种	英语 ; 英语
出版者	Editorial Board of J. of Tsinghua University ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/11916]
专题	清华大学
推荐引用方式 GB/T 7714	Yang Shuping,Lin Xinggang. Key frame extraction using unsupervised clustering based on a statistical model[J],2010, 2010.
APA	Yang Shuping,&Lin Xinggang.(2010).Key frame extraction using unsupervised clustering based on a statistical model..
MLA	Yang Shuping,et al."Key frame extraction using unsupervised clustering based on a statistical model".(2010).

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