1 January 2001 Video shot grouping using best-first model merging
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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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Li Zhao, Li Zhao, Wei Qi, Wei Qi, Yi-Jin Wang, Yi-Jin Wang, Shi-Qiang Yang, Shi-Qiang Yang, HongJiang Zhang, HongJiang Zhang, } "Video shot grouping using best-first model merging", Proc. SPIE 4315, Storage and Retrieval for Media Databases 2001, (1 January 2001); doi: 10.1117/12.410935; https://doi.org/10.1117/12.410935

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