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Proceedings Paper

Video shot grouping using best-first model merging
Author(s): Li Zhao; Wei Qi; Yi-Jin Wang; Shi-Qiang Yang; HongJiang Zhang
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Paper Abstract

For more efficiently organizing, browsing, and retrieving digital video, it is important to extract video structure information at both scene and shot levels. This paper present an effective approach to video scene segmentation based on probabilistic model merging. In our proposed method, we regard the shots in video sequence as hidden state variable and use probabilistic clustering to get the best clustering performance. The experimental results show that our method produces reasonable clustering results based on the visual content. A project named HomeVideo is introduced to show the application of the proposed method for personal video materials management.

Paper Details

Date Published: 1 January 2001
PDF: 8 pages
Proc. SPIE 4315, Storage and Retrieval for Media Databases 2001, (1 January 2001); doi: 10.1117/12.410935
Show Author Affiliations
Li Zhao, Tsinghua Univ. (China)
Wei Qi, Microsoft Research China (China)
Yi-Jin Wang, Tsinghua Univ. (China)
Shi-Qiang Yang, Tsinghua Univ. (China)
HongJiang Zhang, Microsoft Research China (China)


Published in SPIE Proceedings Vol. 4315:
Storage and Retrieval for Media Databases 2001
Minerva M. Yeung; Chung-Sheng Li; Rainer W. Lienhart, Editor(s)

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