Content-Based Video Classification Using Support Vector Machines

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%.

• Type: Conference
• Year: 2004
• Where: ICONIP
• Authors: Vakkalanka Suresh, C. Krishna Mohan, R. Kumara Swamy, B. Yegnanarayana
• Keywords: ICONIP 2004, ICONIP 2007, Optimized Multiclass Classification, Short Video Sequences, TV Broadcast Genre