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International Journal of Digital Multimedia Broadcasting
Volume 2010, Article ID 836357, 20 pages
Research Article

Personalized Sports Video Customization Using Content and Context Analysis

1National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2China-Singapore Institute of Digital Media, Singapore 119615

Received 2 September 2009; Revised 11 December 2009; Accepted 26 January 2010

Academic Editor: Jungong Han

Copyright © 2010 Chao Liang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present an integrated framework on personalized sports video customization, which addresses three research issues: semantic video annotation, personalized video retrieval and summarization, and system adaptation. Sports video annotation serves as the foundation of the video customization system. To acquire detailed description of video content, external web text is adopted to align with the related sports video according to their semantic correspondence. Based on the derived semantic annotation, a user-participant multiconstraint 0/1 Knapsack model is designed to model the personalized video customization, which can unify both video retrieval and summarization with different fusion parameters. As a measure to make the system adaptive to the particular user, a social network based system adaptation algorithm is proposed to learn latent user preference implicitly. Both quantitative and qualitative experiments conducted on twelve broadcast basketball and football videos validate the effectiveness of the proposed method.