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dc.contributor.authorShan, MKen_US
dc.contributor.authorLee, SYen_US
dc.date.accessioned2014-12-08T15:43:58Z-
dc.date.available2014-12-08T15:43:58Z-
dc.date.issued2001-04-01en_US
dc.identifier.issn0167-8655en_US
dc.identifier.urihttp://dx.doi.org/10.1016/S0167-8655(00)00120-3en_US
dc.identifier.urihttp://hdl.handle.net/11536/29734-
dc.description.abstractThe distinguished features of video retrieval lie in the similarity measures and content-based retrieval. Most research on content-based video retrieval represents the content of video as a set of frames, leaving out the temporal ordering of frames in the shot. In this paper, the similarity measures of video content are investigated. We propose a series of similarity measures based on the similarity of frame sequence which take temporal ordering into consideration. All the algorithms corresponding to the similarity measures are based on the approach of dynamic programming. (C) 2001 Published by Elsevier Science B.V.en_US
dc.language.isoen_USen_US
dc.subjectcontent-based video retrievalen_US
dc.subjectsimilarity measureen_US
dc.subjectsequence mappingen_US
dc.subjectdynamic programmingen_US
dc.titleA framework for temporal similarity measures of content-based scene retrievalen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/S0167-8655(00)00120-3en_US
dc.identifier.journalPATTERN RECOGNITION LETTERSen_US
dc.citation.volume22en_US
dc.citation.issue5en_US
dc.citation.spage517en_US
dc.citation.epage532en_US
dc.contributor.department資訊科學與工程研究所zh_TW
dc.contributor.departmentInstitute of Computer Science and Engineeringen_US
dc.identifier.wosnumberWOS:000168148700008-
dc.citation.woscount1-
Appears in Collections:Articles


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