Video Classification in User Profile Generation for Personalized Broadcast Services

摘要

Automatic generation of user profiles, as specified in the MPEG-7 user preference description scheme, for personized broadcast services is investigated in this work. Our research has focused on categorization of user-favored video into different semantically meaningful classes. This knowledge is then used in media filtering guidance and user preferred AV content selection. Several visual and motion features are extracted from source video sequences, such as the number of intra-coded macroblocks, the macroblock motion information, temporal variances and shot activity histograms, for the classification purpose. Moreover, to further improve the accuracy of classification results, a "fuzzy nearest prototype classifier" is applied in this work. It is shown by experimental results that the proposed classification scheme is efficient and accurate.