Automatic summarization of music videos

  • Authors:

  • Xi Shao;Changsheng Xu;Namunu C. Maddage;Qi Tian;Mohan S. Kankanhalli;Jesse S. Jin

  • Affiliations:

  • Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;School of Computing, National University of Singapore;School of Design, Communication and IT, University of Newcastle, Australia

  • Venue:
  • ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
  • Year:
  • 2006

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Abstract

In this article, we propose a novel approach for automatic music video summarization. The proposed summarization scheme is different from the current methods used for video summarization. The music video is separated into the music track and video track. For the music track, a music summary is created by analyzing the music content using music features, an adaptive clustering algorithm, and music domain knowledge. Then, shots in the video track are detected and clustered. Finally, the music video summary is created by aligning the music summary and clustered video shots. Subjective studies by experienced users have been conducted to evaluate the quality of music summaries and effectiveness of the proposed summarization approach. Experiments are performed on different genres of music videos and comparisons are made with the summaries generated based on music track, video track, and manually. The evaluation results indicate that summaries generated using the proposed method are effective in helping realize users' expectations.