Chunxi Liu, Graduate University of Chinese Academy of Sciences, Beijing 100049, China. cxliu@jdl.ac.cn
Qingming Huang, Graduate University of Chinese Academy of Sciences, Beijing 100049, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing 10080, China. qmhuang@jdl.ac.cn
Shuqiang Jiang, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 10080, China. sqjiang@jdl.ac.cn
Weigang Zhang, School of Computer, Harbin Institute of Technology at Weihai, Weihai 264209, China. wgzhang@jdl.ac.cn
Many sports videos such as archery, diving and tennis have repetitive structure patterns. They are reliable clues to generate highlights, summarization and automatic annotation. In this paper, we present a novel approach to analyze these structure patterns in sports video to extract story units. First, an unsupervised scene clustering method for sports video is adopted to automatically categorize the video shots into several disparate scenes. Then, the clustering results are modeled by a transition matrix. Finally, the key scene shots are detected to analyze the structure patterns and extract the story units. Experimental results on several types of broad-cast sports video demonstrate that our approach is effective.
Citation:
Chunxi Liu, Qingming Huang, Shuqiang Jiang, Weigang Zhang, "Extracting Story Units in Sports Video Based on Unsupervised Video Scene Clustering," icme, pp.1605-1608, 2006 IEEE International Conference on Multimedia and Expo, 2006
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