A cartoon video detection method based on active relevance feedback and SVM

Authors:
Xinbo Gao;Jie Li;Na Zhang
Affiliations:
School of Electronic Engineering, Xidian Univ., Xi’an, P.R. China;School of Electronic Engineering, Xidian Univ., Xi’an, P.R. China;School of Electronic Engineering, Xidian Univ., Xi’an, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

By analyzing the particular features of visual content for cartoon videos, 8 typical features of MPEG-7 descriptors are extracted to distinguish the cartoons from other videos. Then, a content-based video classifier is developed by combining the active relevance feedback technique and SVM for detecting the cartoon videos. The experimental results on the vast real video clips illustrate that compared with the classifier based on SVM and that based on traditional relevance feedback technique and SVM, the proposed classifier has a higher advantage of cartoon video detection.