Automated sip detection in naturally-evoked video
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Identification of extremist videos in online video sharing sites
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Text-based video content classification for online video-sharing sites
Journal of the American Society for Information Science and Technology
Baseball event semantic exploring system using HMM
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
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This paper presents an effective and efficient event detection system for broadcast baseball videos. It integrates mid-level cues including scoreboard information and shot transition patterns into event classification rules. First, a simple scoreboard detection and recognition scheme is developed to extract the game status from videos. Then, a shot transition classifier is designed to obtain the shot transition patterns. The extracted mid-level cues are used to develop an event classifier based on a Bayesian Belief Network. Using the inference results of the network, we further derive a set of classification rules to identify baseball events. The set of rules is stored in a look-up table such that the classification is only a simple table look-up operation. The simulation results indicate that it identifies ten significant baseball events with 95% of precision rate and 89% of recall rate, which is very promising.