Structure and event mining in sports video with efficient mosaic
Multimedia Tools and Applications
A framework for flexible summarization of racquet sports video using multiple modalities
Computer Vision and Image Understanding
Event detection in sports video based on generative-discriminative models
EiMM '09 Proceedings of the 1st ACM international workshop on Events in multimedia
Sports video mining via multichannel segmental hidden Markov models
IEEE Transactions on Multimedia
Semantic concept mining in cricket videos for automated highlight generation
Multimedia Tools and Applications
Bayesian belief network based broadcast sports video indexing
Multimedia Tools and Applications
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Video is an information-intensive media with much redundancy. Therefore, it is desirable to be able to mine structure or semantics of video data for efficient browsing, summarization and highlight extraction. In this paper, we propose a generic approach to key-event as well as structure mining for sports video analysis. Mosaic is generated for each shot as the representative image of shot content. Based on mosaic, sports video is mined by the method with prior knowledge and without prior knowledge. Without prior knowledge, our system may locate plays by discriminating those segments without essential content, such as breaks. If prior knowledge is available, the key-events in plays are detected using robust features extracted from mosaic. Experimental results have demonstrated the effectiveness and robustness of this sports video mining approach.