An automatic video content classification scheme based on combined visual features model with modified DAGSVM

Authors:
Xinghao Jiang;Tanfeng Sun;Shilin Wang
Affiliations:
School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China and Shanghai Information Security Management and Technology Research Key Lab, Shanghai, China;School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China and Shanghai Information Security Management and Technology Research Key Lab, Shanghai, China;School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China and Shanghai Information Security Management and Technology Research Key Lab, Shanghai, China
Venue:
Multimedia Tools and Applications
Year:
2011

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Abstract

Automatic video content classification attracts much attention from researchers in multimedia analysis because the management of video content is a challenging task. In this paper, a visual feature representation composed of editing, color, texture and motion features is proposed which is shown to be effective in differentiating among various video contents. A modified Directed Acyclic Graph Support Vector Machine (DAGSVM) model as the classifier is also presented. Experiments show that the features extracted have improved the discriminative ability between different video contents and the computational complexity has also been reduced. By introducing the DAG policy, the performance of the classifier has been enhanced and the classification results demonstrate the precision and effectiveness of this approach, compared with the other two classification methods. In addition, the proposed algorithm can be applied to video searching and harmful-video content filtering, etc.