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A Cartoon Video Detection Method Based on Active SVM Learning |
Gao Xin-bo; Tian Chun-na; Zhang Na |
School of Electronic Engineering, Xidian University, Xi’an 710071, China |
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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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Received: 19 September 2005
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