Abstract
In this paper, classifying and indexing hierarchical video genres using Support Vector Machines (SVMs) are based on only audio features. In fact, segmentation parameters are extracted at block levels, which have a major benefit by capturing local temporal information. The main contribution of our study is to present a powerful combination between the two employed audio descriptors; Mel Frequency Cepstral Coefficients (MFCC) and signal energy in order to classify a big YouTube dataset that includes multi-Arabic dialects video genres and even sub-genres: several sports analysis and various matches categories (foot-ball, basket-ball, hand-ball and volley-ball), both studio and fields news scenes over and above various multi-singer and multi-instruments music clips. Validation of this approach was carried out on over 18 hours of video span yielding a classification accuracy of 98,5% for genres, 97% for sports sub-genres and 76% for music sub-genres. Finally we discuss SVM kernels performance on our proposed dataset.