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Generating comprehensible summaries of rushes sequences based on robust feature matching

Truong, Ba Tu and Venkatesh, Svetha 2007, Generating comprehensible summaries of rushes sequences based on robust feature matching, in TVS 2007 : Proceedings of the International workshop on TRECVID video summarization, ACM, New York, N.Y., pp. 30-34, doi: 10.1145/1290031.1290036.

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Title Generating comprehensible summaries of rushes sequences 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 workshop on TRECVID video summarization (2007 : Augsburg, Germany)
Conference location Augsburg, Germany
Conference dates 28 Sep. 2007
Title of proceedings TVS 2007 : Proceedings of the International workshop on TRECVID video summarization
Editor(s) [Unknown]
Publication date 2007
Conference series International workshop on TRECVID video summarization
Start page 30
End page 34
Total pages 5
Publisher ACM
Place of publication New York, N.Y.
Keyword(s) rushes
Trecvid
video summarization
Summary This paper describes our first attempt at tackling a pilot task in Trecvid: video summarization of rushes data [3]. Our method is based on the tight clustering produced via SIFT matching. In this first attempt, we try to examine how our approach performs without complex implementation in terms of concept detection and excerpt assembly (i.e, no picture-in-picture, split screen and special transitions). Although we do not perform very well in terms of concept inclusion, we rank very well in terms of the summary being easy to understand and relevancy of included segments.
ISBN 9781595937803
Language eng
DOI 10.1145/1290031.1290036
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, ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044598

Document type: Conference Paper
Collection: School of Information Technology
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