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

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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.

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Acknowledgments

The authors wish to thank Miss Dan Qin, Mr. Bin Chen, and Mr. Zhixin Fang for their selfless help. This research is funded by the National Natural Science Foundation of China under grant number of 60702042 and 60802057, and the National 863 Hi-Tech Research and Development plan of China under grant number of 2009AA01Z407.

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Authors and Affiliations

  1. School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xinghao Jiang, Tanfeng Sun & Shilin Wang

  2. Shanghai Information Security Management and Technology Research Key Lab, Shanghai, China

    Xinghao Jiang, Tanfeng Sun & Shilin Wang

Authors
  1. Xinghao Jiang
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  2. Tanfeng Sun
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  3. Shilin Wang
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Correspondence to Tanfeng Sun.

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Jiang, X., Sun, T. & Wang, S. An automatic video content classification scheme based on combined visual features model with modified DAGSVM. Multimed Tools Appl 52, 105–120 (2011). https://doi.org/10.1007/s11042-010-0463-9

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  • DOI: https://doi.org/10.1007/s11042-010-0463-9

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