Content-Based Video Classification Using Support Vector Machines

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Abstract. In this paper, we investigate the problem of video classification into predefined genre. The approach adopted is based on spatial and temporal descriptors derived from short video sequences (20 seconds). By using support vector machines (SVMs), we propose an optimized multiclass classification method. Five popular TV broadcast genre namely cartoon, commercials, cricket, football and tennis are studied. We tested our scheme on more than 2 hours of video data and achieved an accuracy of 92.5%.

Vakkalanka Suresh, C. Krishna Mohan, R. Kumara Swa

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ICONIP 2004 | ICONIP 2007 | Optimized Multiclass Classification | Short Video Sequences | TV Broadcast Genre |

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Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2004
Where	ICONIP
Authors	Vakkalanka Suresh, C. Krishna Mohan, R. Kumara Swamy, B. Yegnanarayana

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Content-Based Video Classification Using Support Vector Machines

ICONIP 2004 | ICONIP 2007 | Optimized Multiclass Classification | Short Video Sequences | TV Broadcast Genre |

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