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Linking identities and viewpoints in home movies based on robust feature matching
conference contribution
posted on 2007-01-01, 00:00 authored by B Truong, Svetha VenkateshSvetha VenkateshThe identification of useful structures in home video is difficult because this class of video is distinguished from other video sources by its unrestricted, non edited content and the absence of regulated storyline. In addition, home videos contain a lot of motion and erratic camera movements, with shots of the same character being captured from various angles and viewpoints. In this paper, we present a solution to the challenging problem of clustering shots and faces in home videos, based on the use of SIFT features. SIFT features have been known to be robust for object recognition; however, in dealing with the complexities of home video setting, the matching process needs to be augmented and adapted. This paper describes various techniques that can improve the number of matches returned as well as the correctness of matches. For example, existing methods for verification of matches are inadequate for cases when a small number of matches are returned, a common situation in home videos. We address this by constructing a robust classifier that works on matching sets instead of individual matches, allowing the exploitation of the geometric constraints between matches. Finally, we propose techniques for robustly extracting target clusters from individual feature matches.
History
Event
International Multimedia Modeling Conference (13th : 2007 : Singapore, Singapore)Pagination
636 - 648Publisher
Springer-Verlag Berlin HeidelbergLocation
Singapore, SingaporePlace of publication
[Berlin, Germany]Publisher DOI
Start date
2007-01-09End date
2007-01-12ISBN-13
9783540694212ISBN-10
3540694218Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2007, Springer-Verlag Berlin, HeidelbergEditor/Contributor(s)
T Cham, J Cai, C Dorai, D Rajan, T ChuaTitle of proceedings
MMM'07 : Advances in multimedia modeling : Proceedings of the 13th International Multimedia Modeling ConferenceUsage metrics
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