HMM Based Automatic Video Classification Using Static and Dynamic Features

Automatic classification of video content is receiving increased impact in the multimedia information processing. This paper inspects the problem of automatic video classification using static and dynamic features. Five different genres such as cartoon, sports, commercials, news and TV serial are studied for assessment. The approach exploits edge information and color histogram as static features and motion information as the dynamic feature with hidden Markov model (HMM) as the classifier. The results are evaluated by constructing individual HMM for each of the features and finally the results obtained are combined to assess the output genre. The method demonstrates the efficiency of the system by applying it on a broad range of video data: 3 hours of video is used for training purpose and a further 1 hour of video as test set. Overall classification accuracy of 95.6% is accomplished.