A spatio-temporal pyramid matching for video retrieval

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dc.contributor.authorChoi, Jaesikko
dc.contributor.authorWang, Ziyuko
dc.contributor.authorLee, Sang-Chulko
dc.contributor.authorJeon, Won J.ko
dc.date.accessioned2019-10-11T01:20:39Z-
dc.date.available2019-10-11T01:20:39Z-
dc.date.created2019-10-10-
dc.date.created2019-10-10-
dc.date.issued2013-06-
dc.identifier.citationCOMPUTER VISION AND IMAGE UNDERSTANDING, v.117, no.6, pp.660 - 669-
dc.identifier.issn1077-3142-
dc.identifier.urihttp://hdl.handle.net/10203/267908-
dc.description.abstractAn efficient video retrieval system is essential to search relevant video contents from a large set of video clips, which typically contain several heterogeneous video clips to match with. In this paper, we introduce a content-based video matching system that finds the most relevant video segments from video database for a given query video clip. Finding relevant video clips is not a trivial task, because objects in a video clip can constantly move over time. To perform this task efficiently, we propose a novel video matching called Spatio-Temporal Pyramid Matching (STPM). Considering features of objects in 2D space and time, STPM recursively divides a video clip into a 3D spatio-temporal pyramidal space and compares the features in different resolutions. In order to improve the retrieval performance, we consider both static and dynamic features of objects. We also provide a sufficient condition in which the matching can get the additional benefit from temporal information. The experimental results show that our STPM performs better than the other video matching methods. (c) 2013 Elsevier Inc. All rights reserved.-
dc.languageEnglish-
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCE-
dc.titleA spatio-temporal pyramid matching for video retrieval-
dc.typeArticle-
dc.identifier.wosid000317538500006-
dc.identifier.scopusid2-s2.0-84875480116-
dc.type.rimsART-
dc.citation.volume117-
dc.citation.issue6-
dc.citation.beginningpage660-
dc.citation.endingpage669-
dc.citation.publicationnameCOMPUTER VISION AND IMAGE UNDERSTANDING-
dc.identifier.doi10.1016/j.cviu.2013.02.003-
dc.contributor.localauthorChoi, Jaesik-
dc.contributor.nonIdAuthorWang, Ziyu-
dc.contributor.nonIdAuthorLee, Sang-Chul-
dc.contributor.nonIdAuthorJeon, Won J.-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorVideo retrieval-
dc.subject.keywordAuthorQuery by video clip-
dc.subject.keywordAuthorHigh-activity videos-
dc.subject.keywordAuthorSport videos-
dc.subject.keywordAuthorPyramid matching-
dc.subject.keywordAuthorSpatio-temporal pyramid matching-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusRECOGNITION-
dc.subject.keywordPlusMODELS-
dc.subject.keywordPlusIMAGE-
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