Article

Generating comprehensible summaries of rushes sequences based on robust feature matching

Authors:

Ba Tu Truong,

Svetha VenkateshAuthors Info & Claims

TVS '07: Proceedings of the international workshop on TRECVID video summarization

Pages 30 - 34

https://doi.org/10.1145/1290031.1290036

Published: 28 September 2007 Publication History

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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.

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D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91--110, 2004.

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[3]

P. Over, A. F. Smeaton, and P. Kelly. The TRECVID 2007 BBC rushes summarization evaluation pilot. In Proceedings of the TRECVID Workshop on Video Summarization (TVS'07), pages 1--15, New York, NY, September 2007. ACM Press.

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B. T. Truong and S. Venkatesh. Finding the optimal segmentation of video sequences. In ICME'05, 2005.

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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.

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B. T. Truong and S. Venkatesh. Video abstraction: A systematic review and classification. ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP), 3(1), Jan 2007.

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Cited By

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Wang FNgo C(2012)Summarizing Rushes Videos by Motion, Object, and Event UnderstandingIEEE Transactions on Multimedia10.1109/TMM.2011.216553114:1(76-87)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1109/TMM.2011.2165531
Li LZhou KXue GZha HYu YSadagopan SRamamritham KKumar ARavindra MBertino EKumar R(2011)Video summarization via transferrable structured learningProceedings of the 20th international conference on World wide web10.1145/1963405.1963448(287-296)Online publication date: 28-Mar-2011
https://dl.acm.org/doi/10.1145/1963405.1963448
Chen BWang JWang J(2009)A novel video summarization based on mining the story-structure and semantic relations among concept entitiesIEEE Transactions on Multimedia10.1109/TMM.2008.200970311:2(295-312)Online publication date: 1-Feb-2009
https://dl.acm.org/doi/10.1109/TMM.2008.2009703
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Index Terms

Generating comprehensible summaries of rushes sequences based on robust feature matching
1. Information systems
  1. Information retrieval
    1. Document representation
2. Theory of computation
  1. Semantics and reasoning
    1. Program reasoning
      1. Abstraction

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Published In

TVS '07: Proceedings of the international workshop on TRECVID video summarization

September 2007

108 pages

ISBN:9781595937803

DOI:10.1145/1290031

General Chairs:
Paul Over
NIST, USA
,
Alan F. Smeaton
Dublin City University, Ireland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 28 September 2007

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MM07: The 15th ACM International Conference on Multimedia 2007

September 28, 2007

Bavaria, Augsburg, Germany

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Wang FNgo C(2012)Summarizing Rushes Videos by Motion, Object, and Event UnderstandingIEEE Transactions on Multimedia10.1109/TMM.2011.216553114:1(76-87)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1109/TMM.2011.2165531
Li LZhou KXue GZha HYu YSadagopan SRamamritham KKumar ARavindra MBertino EKumar R(2011)Video summarization via transferrable structured learningProceedings of the 20th international conference on World wide web10.1145/1963405.1963448(287-296)Online publication date: 28-Mar-2011
https://dl.acm.org/doi/10.1145/1963405.1963448
Chen BWang JWang J(2009)A novel video summarization based on mining the story-structure and semantic relations among concept entitiesIEEE Transactions on Multimedia10.1109/TMM.2008.200970311:2(295-312)Online publication date: 1-Feb-2009
https://dl.acm.org/doi/10.1109/TMM.2008.2009703
Wang TFeng SWang PHu WZhang SZhang WDu YLi JLi JZhang YOver PSmeaton A(2008)THU-intel at rushes summarization of TRECVID 2008Proceedings of the 2nd ACM TRECVid Video Summarization Workshop10.1145/1463563.1463586(124-128)Online publication date: 31-Oct-2008
https://dl.acm.org/doi/10.1145/1463563.1463586
Gorisse DPrecioso FPhilipp-Foliguet SCord MOver PSmeaton A(2008)Summarization scheme based on near-duplicate analysisProceedings of the 2nd ACM TRECVid Video Summarization Workshop10.1145/1463563.1463571(50-54)Online publication date: 31-Oct-2008
https://dl.acm.org/doi/10.1145/1463563.1463571

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Rushes video summarization using audio-visual information and sequence alignment

Rushes video summarization using a collaborative approach

Automatically estimating number of scenes for rushes summarization

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