Generating comprehensible summaries of rushes sequences based on robust feature matching

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Generating comprehensible summaries of rushes sequences based on robust feature matching

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International Multimedia Conference archive
Proceedings of the international workshop on TRECVID video summarization table of contents

Augsburg, Bavaria, Germany

Pages: 30 - 34

Year of Publication: 2007

ISBN:978-1-59593-780-3

Authors

Ba Tu Truong	Curtin University of Technology, Perth, WA, Australia
Svetha Venkatesh	Curtin University of Technology, Perth, Australia

Sponsors

SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 1, Downloads (12 Months): 30, Citation Count: 4

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ABSTRACT

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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Martin A. Fischler , Robert C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, v.24 n.6, p.381-395, June 1981 [doi>10.1145/358669.358692]
	2	David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, v.60 n.2, p.91-110, November 2004 [doi>10.1023/B:VISI.0000029664.99615.94]
	3	Paul Over , Alan F. Smeaton , Philip Kelly, The trecvid 2007 BBC rushes summarization evaluation pilot, Proceedings of the international workshop on TRECVID video summarization, p.1-15, September 28-28, 2007, Augsburg, Bavaria, Germany [doi>10.1145/1290031.1290032]
	4	B. T. Truong and S. Venkatesh. Finding the optimal segmentation of video sequences. In ICME'05, 2005.
	5	B. T. Truong and S. Venkatesh. Linking identities and viewpoints in home videos using robust feature matching. In International Multimedia Modeling Conference (MMM'07), Singapore, Jan 9-12 2007.
	6	Ba Tu Truong , Svetha Venkatesh, Video abstraction: A systematic review and classification, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), v.3 n.1, p.3-es, February 2007 [doi>10.1145/1198302.1198305]

CITED BY 4

	Tao Wang , Shangping Feng , Patricia P. Wang , Wei Hu , Shuang Zhang , Wei Zhang , Yangzhou Du , Jianguo Li , Jianmin Li , Yimin Zhang, THU-intel at rushes summarization of TRECVID 2008, Proceedings of the 2nd ACM TRECVid Video Summarization Workshop, p.124-128, October 31-31, 2008, Vancouver, British Columbia, Canada
	David Gorisse , Frederic Precioso , Sylvie Philipp-Foliguet , Matthieu Cord, Summarization scheme based on near-duplicate analysis, Proceedings of the 2nd ACM TRECVid Video Summarization Workshop, p.50-54, October 31-31, 2008, Vancouver, British Columbia, Canada
	David Gorisse , Frederic Precioso , Sylvie Philipp-Foliguet , Matthieu Cord, Summarization scheme based on near-duplicate analysis, Proceedings of the 2nd ACM TRECVid Video Summarization Workshop, p.50-54, October 31-31, 2008, Vancouver, British Columbia, Canada
	Bo-Wei Chen , Jia-Ching Wang , Jhing-Fa Wang, A novel video summarization based on mining the story-structure and semantic relations among concept entities, IEEE Transactions on Multimedia, v.11 n.2, p.295-312, February 2009