research-article

Comparison of content selection methods for skimming rushes video

Authors:

Werner Bailer,

Georg ThallingerAuthors Info & Claims

TVS '08: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop

Pages 85 - 89

https://doi.org/10.1145/1463563.1463578

Published: 31 October 2008 Publication History

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Abstract

We compare two methods for selecting segments to be included in a video skim, using lists of relevant as well as redundant segments created from different visual features as input. One approach is rule-based, and creates a weighted sum of the input relevances. The other is HMM based, using a model trained on the TRECVID 2007 rushes data. The redundant segments are created from detection of repeated takes and junk content, the selected segments from visual activity and face detection. The results show that the approaches create very short summaries which only contain a part of the relevant information in the video, but reach very high scores in terms of the usability measures non-duplicates, non-junk and pleasant tempo. The HMM based approach contains more information despite shorter duration of the summaries.

References

[1]

W. Bailer. A comparison of distance measures for clustering video sequences. In Proceedings of 1st Workshop on Automated Information Extraction in Media Production, Turin, IT, Sept. 2008.

Digital Library

Google Scholar

[2]

W. Bailer, E. Dumont, S. Essid, and B. Mérialdo. A collaborative approach to automatic rushes video summarization. In Proceedings of IEEE International Conference on Image Processing, San Diego, CA, USA, Sept. 2008.

Crossref

Google Scholar

[3]

W. Bailer, F. Lee, and G. Thallinger. Skimming rushes video using retake detection. In Proceedings of the TRECVID Workshop on Video Summarization (TVS'07), pages 60--64, September 2007.

Digital Library

Google Scholar

[4]

W. Bailer, F. Lee, and G. Thallinger. Detecting and clustering multiple takes of one scene. In MMM, pages 80--89, Kyoto, JP, Jan. 2008.

Digital Library

Google Scholar

[5]

C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001.

Google Scholar

[6]

MPEG-7. Multimedia Content Description Interface-Part 3: Visual. ISO/IEC 15938--3, 2001.

Google Scholar

[7]

NHK STRL. Test modules for TRECVID activity. Use case scenario. Ver.1.2.0E, Apr. 2008.

Google Scholar

[8]

P. Over, A. F. Smeaton, and G. Awad. The TRECVid 2008 BBC rushes summarization evaluation. In TVS '08: Proceedings of the International Workshop on TRECVID Video Summarization, pages 1--20, 2008.

Digital Library

Google Scholar

[9]

A. F. Smeaton and P. Over. TRECVID 2006: Shot boundary detection task overview. In Proceedings of the TRECVID Workshop, November 2006.

Google Scholar

[10]

P. Viola and M. Jones. Fast and robust classification using asymmetric adaboost and a detector cascade. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2002. MIT Press.

Google Scholar

[11]

M. Vlachos, G. Kollios, and D. Gunopoulos. Discovering similar multidimensional trajectories. In ICDE '02: Proceedings of the 18th International Conference on Data Engineering, pages 673--684, San Jose, CA, USA, 2002. IEEE Computer Society.

Digital Library

Google Scholar

Cited By

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Boukadida HBerrani SGros P(2017)Automatically Creating Adaptive Video Summaries Using Constraint Satisfaction ProgrammingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2015.251367827:4(920-934)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1109/TCSVT.2015.2513678
Valdes VMartinez J(2012)Automatic evaluation of video summariesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/2240136.22401388:3(1-21)Online publication date: 6-Aug-2012
https://dl.acm.org/doi/10.1145/2240136.2240138
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
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Index Terms

Comparison of content selection methods for skimming rushes video
1. Information systems
  1. Information retrieval
    1. Document representation
  2. Information systems applications
    1. Multimedia information systems
2. Theory of computation
  1. Semantics and reasoning
    1. Program reasoning
      1. Abstraction

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Published In

TVS '08: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop

October 2008

156 pages

ISBN:9781605583099

DOI:10.1145/1463563

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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Published: 31 October 2008

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MM08: ACM Multimedia Conference 2008

October 31, 2008

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Boukadida HBerrani SGros P(2017)Automatically Creating Adaptive Video Summaries Using Constraint Satisfaction ProgrammingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2015.251367827:4(920-934)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1109/TCSVT.2015.2513678
Valdes VMartinez J(2012)Automatic evaluation of video summariesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/2240136.22401388:3(1-21)Online publication date: 6-Aug-2012
https://dl.acm.org/doi/10.1145/2240136.2240138
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
Hu WXie NLi Zeng XMaybank S(2011)A Survey on Visual Content-Based Video Indexing and RetrievalIEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews10.1109/TSMCC.2011.210971041:6(797-819)Online publication date: 1-Nov-2011
https://dl.acm.org/doi/10.1109/TSMCC.2011.2109710
Zhou HSadka ASwash MAzizi JSadiq U(2010)Feature extraction and clustering for dynamic video summarisationNeurocomputing10.1016/j.neucom.2009.09.02273:10-12(1718-1729)Online publication date: 1-Jun-2010
https://dl.acm.org/doi/10.1016/j.neucom.2009.09.022
Over PSmeaton AAwad GOver PSmeaton A(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
https://dl.acm.org/doi/10.1145/1463563.1463564

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Skimming rushes video using retake detection

Rushes video summarization using a collaborative approach

Regim, research group on intelligent machines, tunisia, at TRECVID 2008, BBC rushes summarization

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