Robust Scene Extraction Using Multi-Stream HMMs for Baseball Broadcast(Image Processing and Video Processing)
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概要
- 論文の詳細を見る
In this paper, we propose a robust statistical framework for extracting scenes from a baseball broadcast video. We apply multi-stream hidden Markov models (HMMs) to control the weights among different features. To achieve a large robustness against new scenes, we used a common simple structure for all the HMMs. In addition, scene segmentation and unsupervised adaptation were applied to achieve greater robustness against differences in environmental conditions among games. The F-measure of scene-extracting experiments for eight types of scene from 4.5 hours of digest data was 77.4% and was increased to 78.7% by applying scene segmentation. Furthermore, the unsupervised adaptation method improved precision by 2.7 points to 81.4%. These results confirm the effectiveness of our framework.
- 社団法人電子情報通信学会の論文
- 2006-09-01
著者
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Bach Nguyen
Department Of Computer Science Tokyo Institute Of Technology:(present Office)ntt Communications
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SHINODA Koichi
Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Instit
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FURUI Sadaoki
Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Instit
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Shinoda Koichi
Department Of Computer Science Tokyo Institute Of Technology
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Shinoda Koichi
Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institut
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Furui Sadaoki
Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institut
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Furui Sadaoki
Department Of Computer Science Tokyo Institute Of Technology
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