Video genre classification using weighted kernel logistic regression

Authors:
Ahmed A. M. Hamed;Renfa Li;Zhang Xiaoming;Cheng Xu
Affiliations:
Hunan University, Changsha, China and University of Bahri, Khartoum, Sudan;Hunan University, Changsha, China;Hunan University, Changsha, China;Hunan University, Changsha, China
Venue:
Advances in Multimedia - Special issue on Multimedia Applications for Smart Device in Ubiquitous Environments
Year:
2013

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Abstract

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.