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Hierarchical modeling and adaptive clustering for real-time summarization of rush videos in trecvid'08

Published: 31 October 2008 Publication History

Abstract

In this paper, our techniques used in TRECVID'08 on BBC rush summarization are described. Firstly, rush videos are hierarchical modeled using formal language description. Then, shot detection and V-unit determination are applied for video structuring; junk frames within the model are also effectively removed. Thirdly, adaptive clustering is employed to group shots into clusters to remove retakes. Then, each selected shot is ranked according to its length and sum of activity level for summarization. Competitive results have proved the effectiveness and efficiency of our techniques fully implemented in compressed-domain.

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  • (2023)A comprehensive study of automatic video summarization techniquesArtificial Intelligence Review10.1007/s10462-023-10429-z56:10(11473-11633)Online publication date: 13-Mar-2023
  • (2016)Detection of the Periodicity of Human Actions for Efficient Video Summarization2016 IEEE International Symposium on Multimedia (ISM)10.1109/ISM.2016.0086(391-394)Online publication date: Dec-2016
  • (2016)Redundancy Elimination in Video SummarizationImage Feature Detectors and Descriptors10.1007/978-3-319-28854-3_7(173-202)Online publication date: 23-Feb-2016
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cover image ACM Conferences
TVS '08: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
October 2008
156 pages
ISBN:9781605583099
DOI:10.1145/1463563
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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Publication History

Published: 31 October 2008

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Author Tags

  1. adaptive clustering
  2. rush video summarization
  3. spatio-temporal signature
  4. trecvid

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MM08
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MM08: ACM Multimedia Conference 2008
October 31, 2008
British Columbia, Vancouver, Canada

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Cited By

View all
  • (2023)A comprehensive study of automatic video summarization techniquesArtificial Intelligence Review10.1007/s10462-023-10429-z56:10(11473-11633)Online publication date: 13-Mar-2023
  • (2016)Detection of the Periodicity of Human Actions for Efficient Video Summarization2016 IEEE International Symposium on Multimedia (ISM)10.1109/ISM.2016.0086(391-394)Online publication date: Dec-2016
  • (2016)Redundancy Elimination in Video SummarizationImage Feature Detectors and Descriptors10.1007/978-3-319-28854-3_7(173-202)Online publication date: 23-Feb-2016
  • (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
  • (2011)LIVE: An Integrated Production and Feedback System for Intelligent and Interactive TV BroadcastingIEEE Transactions on Broadcasting10.1109/TBC.2011.215825257:3(646-661)Online publication date: Sep-2011
  • (2010)STIMOMultimedia Tools and Applications10.1007/s11042-009-0307-746:1(47-69)Online publication date: 1-Jan-2010
  • (2009)Hierarchical modeling and adaptive clustering for real-time summarization of rush videosIEEE Transactions on Multimedia10.1109/TMM.2009.202178211:5(906-917)Online publication date: 1-Aug-2009
  • (2008)The trecvid 2008 BBC rushes summarization evaluationProceedings of the 2nd ACM TRECVid Video Summarization Workshop10.1145/1463563.1463564(1-20)Online publication date: 31-Oct-2008

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