A Cartoon Video Detection Method Based on Active SVM Learning

Gao Xin-bo; Tian Chun-na; Zhang Na

doi:10.3724/SP.J.1146.2005.01193

Volume 29 Issue 6

Jan. 2011

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Gao Xin-bo, Tian Chun-na, Zhang Na. A Cartoon Video Detection Method Based on Active SVM Learning[J]. Journal of Electronics & Information Technology, 2007, 29(6): 1338-1342. doi: 10.3724/SP.J.1146.2005.01193

Citation:

Gao Xin-bo, Tian Chun-na, Zhang Na. A Cartoon Video Detection Method Based on Active SVM Learning[J]. Journal of Electronics & Information Technology, 2007, 29(6): 1338-1342. doi: 10.3724/SP.J.1146.2005.01193

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A Cartoon Video Detection Method Based on Active SVM Learning

doi: 10.3724/SP.J.1146.2005.01193

Received Date: 2005-09-19
Rev Recd Date: 2006-11-22
Publish Date: 2007-06-19

Abstract

Abstract

Through analyzing visual differences between cartoon and other videos, 8 groups of typical features including MPEG-7 descriptors are extracted to construct the feature space of cartoon videos. Then, a content-based video classifier is designed by introducing the active relevance feedback technique into Support Vector Machine (SVM) for the cartoon video detection. Experimental results on a great many real video clips illustrate that the constructed feature space can represent the cartoon videos effectively. In addition, compared with the classifier based on SVM and that based on the traditional relevance feedback technique and SVM, the proposed classifier has a higher performance for cartoon video detection.

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References(1)

References

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