Event recognition based on a local space-time interest points and self-organization feature map method

Authors:
Yi-Lan Guo;Ji-Xiang Du;Chuan-Min Zhai
Affiliations:
Department of Computer Science and Technology, Huaqiao University, Xiamen, China;Department of Computer Science and Technology, Huaqiao University, Xiamen, China;Department of Computer Science and Technology, Huaqiao University, Xiamen, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Year:
2011

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Abstract

This paper proposes a novel method based on local space-time interest points and self-organization feature map to recognize and retrieval complex events in real movies. In this method, an individual video sequence is represented as a SOFM density map then we integrate such density map with SVM for recognition events. Local space-time features are introduced to capture the local events in video and can be adapted to size and velocity of the pattern of the event. To evaluate effectiveness of this method, this paper uses the public Hollywood dataset, in this dataset the shot sequences has collected from 32 different Hollywood movies and it includes 8 event classes. The presented result justify the proposed method explicitly improve the average accuracy and average precision compared to other relative approaches.