Joint shot boundary detection and key frame extraction

Liu, X; Song, M; Zhang, L; Wang, S; Bu, J; Chen, C; Tao, D

Joint shot boundary detection and key frame extraction

Liu, X Song, M Zhang, L Wang, S Bu, J Chen, C Tao, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2012, pp. 2565 - 2568
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Song, M	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Bu, J	en_US
dc.contributor.author	Chen, C	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2012, pp. 2565 - 2568	en_US
dc.identifier.isbn	9784990644109	en_US
dc.identifier.issn	1051-4651	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119897
dc.description.abstract	Representing a video by a set of key frames is useful for efficient video browsing and retrieving. But key frame extraction keeps a challenge in the computer vision field. In this paper, we propose a joint framework to integrate both shot boundary detection and key frame extraction, wherein three probabilistic components are taken into account, i.e. The prior of the key frames, the conditional probability of shot boundaries and the conditional probability of each video frame. Thus the key frame extraction is treated as a Maximum A Posteriori which can be solved by adopting alternate strategy. Experimental results show that the proposed method preserves the scene level structure and extracts key frames that are representative and discriminative. © 2012 ICPR Org Committee.	en_US
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition	en_US
dc.title	Joint shot boundary detection and key frame extraction	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Representing a video by a set of key frames is useful for efficient video browsing and retrieving. But key frame extraction keeps a challenge in the computer vision field. In this paper, we propose a joint framework to integrate both shot boundary detection and key frame extraction, wherein three probabilistic components are taken into account, i.e. The prior of the key frames, the conditional probability of shot boundaries and the conditional probability of each video frame. Thus the key frame extraction is treated as a Maximum A Posteriori which can be solved by adopting alternate strategy. Experimental results show that the proposed method preserves the scene level structure and extracts key frames that are representative and discriminative. © 2012 ICPR Org Committee.

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http://hdl.handle.net/10453/119897