Semantic principal video shot classification via mixture Gaussian

  • Authors:

  • Hangzai Luo;Jianping Fan;Jing Xiao;Xingquan Zhu

  • Affiliations:

  • Dept. of Comput. Sci., North Carolina Univ., Charlotte, NC, USA;Dept. of Comput. Sci., North Carolina Univ., Charlotte, NC, USA;Dept. of Comput. Sci., North Carolina Univ., Charlotte, NC, USA;Dept. of Electron. & Informatics, Ryukoku Univ., Shiga, Japan

  • Venue:
  • ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
  • Year:
  • 2003

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Abstract

As digital cameras become more affordable, digital video now plays an important role in medical education and healthcare. In this paper, we propose a novel framework to facilitate semantic classification of surgery education videos. Specifically, the framework includes: (a) semantic-sensitive video content characterization via principal video shots, (b) semantic video classification via a mixture Gaussian model to bridge the semantic gap between low-level visual features and semantic visual concepts in a specific surgery education video domain.