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Linking identities and viewpoints in home movies based on robust feature matching

Truong, Ba Tu and Venkatesh, Svetha 2007, Linking identities and viewpoints in home movies based on robust feature matching, in MMM'07 : Advances in multimedia modeling : Proceedings of the 13th International Multimedia Modeling Conference, Springer-Verlag Berlin Heidelberg, [Berlin, Germany], pp. 636-648, doi: 10.1007/978-3-540-69423-6_62.

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Title Linking identities and viewpoints in home movies based on robust feature matching
Author(s) Truong, Ba Tu
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Multimedia Modeling Conference (13th : 2007 : Singapore, Singapore)
Conference location Singapore, Singapore
Conference dates 9-12 Jan. 2007
Title of proceedings MMM'07 : Advances in multimedia modeling : Proceedings of the 13th International Multimedia Modeling Conference
Editor(s) Cham, Tat-Jen
Cai, Jianfei
Dorai, Chitra
Rajan, Deepu
Chua, Tat-Seng
Publication date 2007
Conference series International Multimedia Modeling Conference
Start page 636
End page 648
Total pages 13
Publisher Springer-Verlag Berlin Heidelberg
Place of publication [Berlin, Germany]
Keyword(s) home video
SIFT feature
object recognition
Summary The 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.
ISBN 9783540694212
3540694218
Language eng
DOI 10.1007/978-3-540-69423-6_62
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2007, Springer-Verlag Berlin, Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044776

Document type: Conference Paper
Collection: School of Information Technology
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