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Advances in Multimedia
Volume 2013, Article ID 653687, 6 pages
Research Article

Video Genre Classification Using Weighted Kernel Logistic Regression

1Hunan University, Changsha 410082, China
2University of Bahri, Khartoum 11123, Sudan

Received 28 March 2013; Accepted 10 July 2013

Academic Editor: Tai-hoon Kim

Copyright © 2013 Ahmed A. M. Hamed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Due to the widening semantic gap of videos, computational tools to classify these videos into different genre are highly needed to narrow it. Classifying videos accurately demands good representation of video data and an efficient and effective model to carry out the classification task. Kernel Logistic Regression (KLR), kernel version of logistic regression (LR), proves its efficiency as a classifier, which can naturally provide probabilities and extend to multiclass classification problems. In this paper, Weighted Kernel Logistic Regression (WKLR) algorithm is implemented for video genre classification to obtain significant accuracy, and it shows accurate and faster good results.