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
Parallel neural networks for multimodal video genre classification
Multimedia Tools and Applications
Automatic sports genre categorization and view-type classification over large-scale dataset
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A multidimensional approach to detect action scene in video data
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Statistical motion information extraction and representation for semantic video analysis
IEEE Transactions on Circuits and Systems for Video Technology
Sports classification using cross-ratio histograms
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Content-based video genre classification using multiple cues
Proceedings of the 3rd international workshop on Automated information extraction in media production
Automatic video genre categorization and event detection techniques on large-scale sports data
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
Ice hockey shooting event modeling with mixture hidden Markov model
Multimedia Tools and Applications
A Generic Approach for Systematic Analysis of Sports Videos
ACM Transactions on Intelligent Systems and Technology (TIST)
Multimodal genre classification of TV programs and YouTube videos
Multimedia Tools and Applications
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Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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Building a generic content-based sports video analysis system remains a challenging problem because of the diversity in sports rules and game features which makes it difficult to discover generic low-level features or high-level modeling algorithms. One possible alternative is to first classify the sports genre and then apply specific sports domain knowledge to perform analysis. In this paper we describe a multi-level framework to automatically recognize the genre of the sports video. The system consists of a Pseudo-2D-HMM classifier using low-level visual/audio features to evaluate the video clips. The experimental results are satisfactory and extension of the framework to a generic sports video analysis system is being implemented.