Audio-Video Based Classification Using SVM
The IUP Journal of Science & Technology, Vol. 7, No. 1, pp. 44-53, March 2011
Posted: 9 Jun 2011
Date Written: June 9, 2011
Abstract
This paper presents a method to classify audio-video data into one of five classes: advertisement, cartoon, news, movie and songs. Automatic audio-video classification is very useful to audio-video indexing and content based audio-video retrieval. Mel Frequency Cepstral Coefficients (MFCC) are used to characterize the audio data. The color histogram features extracted from the images in the video clips are used as visual features. Support Vector Machine (SVM) is used for audio and video classification. The experiments on different genres illustrate that the results of classification are significant and effective. Experimental results of audio classification and video classification are combined using weighted sum rule for audio-video based classification. The method classifies the audio-video clips with an accuracy of 87.0%.
Keywords: Support vector machines (SVM), Mel Frequency Cepstral Coefficients (MFCC), Color histogram, Audio classification, Video classification, Audio-video classification
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