Automatic sports genre categorization and view-type classification over large-scale dataset

  • Authors:

  • Lingfang Li;Ning Zhang;Ling-Yu Duan;Qingming Huang;Jun Du;Ling Guan

  • Affiliations:

  • Graduate University of Chinese Academy of Sciences, Beijing, China;Ryerson Multimedia Research Laboratory, Ryerson University, Toronto, Ontario, Toronto, Canada;Peking University, Beijing, China;Graduate School of Chinese Academy of Sciences, Beijing, China;NEC Reasearch Labs China, Beijing, China;Ryerson Multimedia Research Laboratory, Ryerson University, Toronto, Ontario, Toronto, Canada

  • Venue:
  • MM '09 Proceedings of the 17th ACM international conference on Multimedia
  • Year:
  • 2009

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Abstract

This paper presents a framework with two automatic tasks targeting large-scale and low quality sports video archives collected from online video streams. The framework is based on the bag of visual-words model using speeded-up robust features (SURF). The first task is sports genre categorization based on hierarchical structure. Following on the second task which is based on automatically obtained genre, views are classified using support vector machines (SVMs). As a consequence, the views classification result can be used in video parsing and highlight extraction. As compared with state-of-the-art methods, our approach is fully automatic as well as domain knowledge free and thus provides a better extensibility. Furthermore, our dataset consists of 14 sport genres with 6850 minutes in total. Both sport genre categorization and view type classification have more than 80% accuracy rates, which validate this framework's robustness and potential in web-based applications.