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Clever clustering vs. simple speed-up for summarizing rushes
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International Multimedia Conference archive
Proceedings of the international workshop on TRECVID video summarization table of contents
Augsburg, Bavaria, Germany
Pages: 20 - 24  
Year of Publication: 2007
ISBN:978-1-59593-780-3
Authors
Alexander G. Hauptmann  Carnegie Mellon University, Pittsburgh, PA
Michael G. Christel  Carnegie Mellon University, Pittsburgh, PA
Wei-Hao Lin  Carnegie Mellon University, Pittsburgh, PA
Bryan Maher  Carnegie Mellon University, Pittsburgh, PA
Jun Yang  Carnegie Mellon University, Pittsburgh, PA
Robert V. Baron  Carnegie Mellon University, Pittsburgh, PA
Guang Xiang  Carnegie Mellon University, Pittsburgh, PA
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): 0,   Downloads (12 Months): 22,   Citation Count: 8
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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.



CITED BY  8

Collaborative Colleagues:
Alexander G. Hauptmann: colleagues
Michael G. Christel: colleagues
Wei-Hao Lin: colleagues
Bryan Maher: colleagues
Jun Yang: colleagues
Robert V. Baron: colleagues
Guang Xiang: colleagues