Near-Duplicate Video Clip Detection Using Model-Free Semantic Concept Detection and Adaptive Semantic Distance Measurement

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dc.contributor.authorMin, Hyun-seokko
dc.contributor.authorChoi, Jae-Youngko
dc.contributor.authorDe Neve, Wesleyko
dc.contributor.authorRo, Yong-Manko
dc.date.accessioned2013-03-12T15:52:24Z-
dc.date.available2013-03-12T15:52:24Z-
dc.date.created2012-10-09-
dc.date.created2012-10-09-
dc.date.issued2012-08-
dc.identifier.citationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, v.22, no.8, pp.1174 - 1187-
dc.identifier.issn1051-8215-
dc.identifier.urihttp://hdl.handle.net/10203/102783-
dc.description.abstractMotivated by the observation that content transformations tend to preserve the semantic information conveyed by video clips, this paper introduces a novel technique for near-duplicate video clip (NDVC) detection, leveraging model-free semantic concept detection and adaptive semantic distance measurement. In particular, model-free semantic concept detection is realized by taking advantage of the collective knowledge in an image folksonomy (which is an unstructured collection of user-contributed images and tags), facilitating the use of an unrestricted concept vocabulary. Adaptive semantic distance measurement is realized by means of the signature quadratic form distance (SQFD), making it possible to flexibly measure the similarity between video shots that contain a varying number of semantic concepts, and where these semantic concepts may also differ in terms of relevance and nature. Experimental results obtained for the MIRFLICKR-25000 image set (used as a source of collective knowledge) and the TRECVID 2009 video set (used to create query and reference video clips) demonstrate that model-free semantic concept detection and SQFD can be successfully used for the purpose of identifying NDVCs.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.subjectCOPY DETECTION-
dc.subjectIMAGE RETRIEVAL-
dc.subjectANNOTATION-
dc.subjectSEARCH-
dc.subjectSEQUENCES-
dc.subjectFEATURES-
dc.titleNear-Duplicate Video Clip Detection Using Model-Free Semantic Concept Detection and Adaptive Semantic Distance Measurement-
dc.typeArticle-
dc.identifier.wosid000308437500006-
dc.identifier.scopusid2-s2.0-84864710048-
dc.type.rimsART-
dc.citation.volume22-
dc.citation.issue8-
dc.citation.beginningpage1174-
dc.citation.endingpage1187-
dc.citation.publicationnameIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY-
dc.identifier.doi10.1109/TCSVT.2012.2197080-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorRo, Yong-Man-
dc.contributor.nonIdAuthorDe Neve, Wesley-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorCollective knowledge-
dc.subject.keywordAuthorimage folksonomy-
dc.subject.keywordAuthornear-duplicate video clip detection-
dc.subject.keywordAuthorsemantic concept detection-
dc.subject.keywordAuthorsemantic distance-
dc.subject.keywordAuthorsemantic video signature-
dc.subject.keywordAuthorvideo copy detection-
dc.subject.keywordPlusCOPY DETECTION-
dc.subject.keywordPlusIMAGE RETRIEVAL-
dc.subject.keywordPlusANNOTATION-
dc.subject.keywordPlusSEARCH-
dc.subject.keywordPlusSEQUENCES-
dc.subject.keywordPlusFEATURES-
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