Learning video preferences from video content
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
Accumulated motion energy fields estimation and representation for semantic event detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Automatic sports genre categorization and view-type classification over large-scale dataset
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Statistical motion information extraction and representation for semantic video analysis
IEEE Transactions on Circuits and Systems for Video Technology
Text-based video content classification for online video-sharing sites
Journal of the American Society for Information Science and Technology
An effective method for video genre classification
Proceedings of the ACM International Conference on Image and Video Retrieval
Ice hockey shooting event modeling with mixture hidden Markov model
Multimedia Tools and Applications
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In this paper we address the problem of sports video classification using hidden Markov models (HMMs). For each sports genre, we construct two HMMs representing motion and color features respectively. The observation sequences generated from the principal motion direction and the principal color of each frame are fed to a motion and a color HMM respectively. The outputs are integrated to make a final decision. We tested our scheme on 220 minutes of sports video with four genre types: ice hockey, basketball, football, and soccer, and achieved an overall classification accuracy of 93%.