Please use this identifier to cite or link to this item: https://doi.org/10.1145/1631272.1631295
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dc.titleScalable detection of partial near-duplicate videos by visual-temporal consistency
dc.contributor.authorTan, H.-K.
dc.contributor.authorNgo, C.-W.
dc.contributor.authorHong, R.
dc.contributor.authorChua, T.-S.
dc.date.accessioned2013-07-04T08:14:37Z
dc.date.available2013-07-04T08:14:37Z
dc.date.issued2009
dc.identifier.citationTan, H.-K.,Ngo, C.-W.,Hong, R.,Chua, T.-S. (2009). Scalable detection of partial near-duplicate videos by visual-temporal consistency. MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums : 145-154. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/1631272.1631295" target="_blank">https://doi.org/10.1145/1631272.1631295</a>
dc.identifier.isbn9781605586083
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40885
dc.description.abstractFollowing the exponential growth of social media, there now exist huge repositories of videos online. Among the huge volumes of videos, there exist large numbers of near-duplicate videos. Most existing techniques either focus on the fast retrieval of full copies or near-duplicates, or consider localization in a heuristic manner. This paper considers the scalable detection and localization of partial near-duplicate videos by jointly considering visual similarity and temporal consistency. Temporal constraints are embedded into a network structure as directed edges. Through the structure, partial alignment is novelly converted into a network flow problem where highly efficient solutions exist. To precisely decide the boundaries of the overlapping segments, pair-wise constraints generated from keypoint matching can be added to the network to iteratively refine the localization result. We demonstrate the effectiveness of partial alignment for three different tasks. The first task links partial segments in full-length movies to videos crawled from YouTube. The second task performs fast web video search, while the third performs near-duplicate shot and copy detection. The experimental result demonstrates the effectiveness and efficiency of the proposed method compared to state-of-the-art techniques. Copyright 2009 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/1631272.1631295
dc.sourceScopus
dc.subjectNetwork flow
dc.subjectPartial near-duplicate
dc.subjectTemporal graph
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/1631272.1631295
dc.description.sourcetitleMM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
dc.description.page145-154
dc.identifier.isiutNOT_IN_WOS
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