TV program segmentation using multi-modal information fusion

  • Authors:

  • Hongliang Bai;Lezi Wang;Gang Qin;Jiwei Zhang;Kun Tao;Xiaofu Chang;Yuan Dong

  • Affiliations:

  • France Telecom research & development - Beijing, P. R. China;Beijing University of Posts and Telecommunications, P. R. China;Beijing University of Posts and Telecommunications, P. R. China;Beijing University of Posts and Telecommunications, P. R. China;France Telecom research & development - Beijing, P. R. China;France Telecom research & development - Beijing, P. R. China;France Telecom research & development - Beijing, P. R. China and Beijing University of Posts and Telecommunications, P. R. China

  • Venue:
  • Proceedings of the 1st ACM International Conference on Multimedia Retrieval
  • Year:
  • 2011

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Abstract

A TV program segmentation algorithm is presented by the fusion of the multi-modal information in the large-scale videos. As "Inter-Programs" are generally inserted into the TV videos repeatedly, the macro structures of the videos can be effectively and automatically generated by identifying the video-audio features of the special sequences. The Electronic Program Guide (EPG) is used to organize the structures into the programs. Three sections are included in the algorithm, namely, the video-based non-supervised duplicate sequence detection, the audio-based special clip retrieval and the EPG-based 24-hour program segmentation. The algorithm has been tested in 60-day different-type TV videos. The F-measures of the multi-modal fusion and video-based duplicated sequence detection achieve the rates of over 98% and 96% respectively. These results show that the proposed method is highly efficient and effective for the TV Program segmentation.