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|dc.title||A mid-level representation framework for semantic sports video analysis|
|dc.identifier.citation||Duan, L.-Y.,Xu, M.,Chua, T.-S.,Tian, Q.,Xu, C.-S. (2003). A mid-level representation framework for semantic sports video analysis. Proceedings of the ACM International Multimedia Conference and Exhibition : 33-44. ScholarBank@NUS Repository.|
|dc.description.abstract||Sports video has been widely studied due to its tremendous commercial potentials. Despite encouraging results from various specific sports games, it is almost impossible to extend a system for a new sports game because they usually employ different sets of low-level features appropriate for the specific games and closely coupled with the use of game specific rules to detect events or highlights. There is a lack of internal representation and structure to be generic and applicable for many different sports. In this paper, we present a generic mid-level representation framework for semantic sports video analysis. The mid-level representation layer is introduced between the low-level audio-visual processing and high-level semantic analysis. It allows us to separate sports specific knowledge and rules from the low-level and mid-level feature extraction. This makes sports video analysis more efficient, effective, and less ad-hoc for various types of sports. To achieve robustness of the low-level feature analysis, a non-parametric clustering, mean shift procedure, has been successfully applied to both color and motion analysis. The proposed framework has been tested for five field-ball type sports covering duration of about 8 hours. Experiments have shown its robust performance in semantic analysis and event detection. We believe that the proposed mid-level representation framework can be used for event detection, highlight extraction, summarization and personalization of many types of sports video.|
|dc.description.sourcetitle||Proceedings of the ACM International Multimedia Conference and Exhibition|
|Appears in Collections:||Staff Publications|
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