Video Genre Classification Using Weighted Kernel Logistic Regression
Joint Authors
Li, Renfa
Xu, Cheng
Xiaoming, Zhang
Hamed, Ahmed A. M.
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-09
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Hamed, Ahmed A. M.& Li, Renfa& Xiaoming, Zhang& Xu, Cheng. 2013. Video Genre Classification Using Weighted Kernel Logistic Regression. Advances in Multimedia،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-488552
Modern Language Association (MLA)
Hamed, Ahmed A. M.…[et al.]. Video Genre Classification Using Weighted Kernel Logistic Regression. Advances in Multimedia No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-488552
American Medical Association (AMA)
Hamed, Ahmed A. M.& Li, Renfa& Xiaoming, Zhang& Xu, Cheng. Video Genre Classification Using Weighted Kernel Logistic Regression. Advances in Multimedia. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-488552
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-488552