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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Gao, X., Li, J., Zhang, N. (2006). A Cartoon Video Detection Method Based on Active Relevance Feedback and SVM. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_63
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DOI: https://doi.org/10.1007/11760023_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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