A rough set approach to video genre classification

Authors:
Wengang Cheng;Chang'an Liu;Xingbo Wang
Affiliations:
Department of Computer Science, North China Electric Power Univ., Beijing, China;Department of Computer Science, North China Electric Power Univ., Beijing, China;Department of Computer Science, North China Electric Power Univ., Beijing, China
Venue:
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Year:
2006

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Abstract

Video classification provides an efficient way to manage and utilize the video data. Existing works on this topic fall into this category: enlarging the feature set until the classification is reliable enough. However, some features may be redundant or irrelevant. In this paper, we address the problem of choosing efficient feature set in video genre classification to achieve acceptable classification results but relieve computation burden significantly. A rough set approach is proposed. In comparison with existing works and the decision tree method, experimental results verify the efficiency of the proposed approach.