Video Genre Classification Using Weighted Kernel Logistic Regression | Zendy

Ahmed A. Hamed | Zendy; Renfa Li | Zendy; Xiaoming Zhang | Zendy; Cheng Xu | Zendy

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Video Genre Classification Using Weighted Kernel Logistic Regression

Author(s) -

Ahmed A. Hamed,

Renfa Li,

Xiaoming Zhang,

Cheng Xu

Publication year - 2013

Publication title -

advances in multimedia

Language(s) - English

Resource type - Journals

eISSN - 1687-5699

pISSN - 1687-5680

DOI - 10.1155/2013/653687

Subject(s) - logistic regression , artificial intelligence , classifier (uml) , computer science , logistic model tree , kernel (algebra) , pattern recognition (psychology) , regression , kernel method , multiclass classification , machine learning , support vector machine , mathematics , statistics , combinatorics

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

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