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Clever clustering vs. simple speed-up for summarizing rushes

Published:28 September 2007Publication History

ABSTRACT

This paper discusses in detail our approaches for producing the submitted summaries to TRECVID, including the two baseline methods. The cluster method performed well in terms of coverage, and adequately in terms of user satisfaction, but did take longer to review. We conducted additional evaluations using the same TRECVID assessment interface to judge 2 additional methods for summary generation: 25x (simple speed-up by 25 times), and pz (emphasizing pans and zooms). Human assessors show significant differences between the cluster, pz, and 25x approaches. The best coverage (text inclusion performance) is obtained by 25x, but at the expense of taking the most time to evaluate and perceived as the most redundant. Method pz was easier to use than cluster and had better performance on pan/zoom recall tasks, leading into discussions on how summaries can be improved with more knowledge of the anticipated users and tasks.

References

  1. Christel, M.G., Smith, M.A., Taylor, C.R, & Winkler, D.B. Evolving Video Skims into Useful Multimedia Abstractions. In Proc. ACM CHI '98 (Los Angeles, April 1998), 171--178. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. P. Over, P., Smeaton, A.F. and Kelly, P. The TRECVID 2007 BBC rushes summarization evaluation pilot. In Proceedings of the TRECVID Workshop on Video Summarization (TVS'07), Augsburg, Germany, September 28, 2007, ACM Press, New York, NY, 2007, 1--15 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Hanjalic, A. Shot-Boundary Detection: Unraveled or Resolved? In IEEE Transactions on Circuits and Systems for Video Technology. 12(2). 2002. 90--105. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Clever clustering vs. simple speed-up for summarizing rushes

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          cover image ACM Conferences
          TVS '07: Proceedings of the international workshop on TRECVID video summarization
          September 2007
          108 pages
          ISBN:9781595937803
          DOI:10.1145/1290031

          Copyright © 2007 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 28 September 2007

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