Shot type classification in sports video using fuzzy information granular

Authors:
Congyan Lang;De Xu;Wengang Cheng;Yiwei Jiang
Affiliations:
Department of Computer Science, Beijing Jiaotong University, Beijing, P.R. China;Department of Computer Science, Beijing Jiaotong University, Beijing, P.R. China;Department of Computer Science, Beijing Jiaotong University, Beijing, P.R. China;Department of Computer Science, Beijing Jiaotong University, Beijing, P.R. China
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2005

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Abstract

In this paper, we present a new method for classifying shot type in sports video using fuzzy information granular. The problem is important for applications such as video structure analysis and content understanding. In particular,two-stage off-line learning processes perform knowledge extraction of semantic concepts and automatic shot classification, respectively. In the first stage, the extracted prominent regions are used as a good pattern in semantic concept level. Then a number of global features are defined as efficient input of the shot type classifier in the second stage. The identification of semantic concepts and classification of shot are based on soft decisions. Hence, this framework can adequately capture the uncertainty or ambiguity of scales of a shot. Experimental results show the excellent performance of the approach.