Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
Event Detection and Summarization in Sports Video
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Maximum entropy model-based baseball highlight detection and classification
Computer Vision and Image Understanding - Special issue on event detection in video
Sports video categorizing method using camera motion parameters
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Robust scene recognition using language models for scene contexts
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Robust Scene Extraction Using Multi-Stream HMMs for Baseball Broadcast
IEICE - Transactions on Information and Systems
A robust scene recognition system for baseball broadcast using data-driven approach
Proceedings of the 6th ACM international conference on Image and video retrieval
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Event based indexing of broadcasted sports video by intermodalcollaboration
IEEE Transactions on Multimedia
Content analysis of video using principal components
IEEE Transactions on Circuits and Systems for Video Technology
Automatically estimating number of scenes for rushes summarization
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
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We propose a robust score scene detection method for baseball broadcast videos. This method is based on the data-driven approach which has been successful in statistical speech recognition. Audio and video feature streams are integrated by a multi-stream hidden Markov model to model each scene. The proposed method was evaluated in score scene detection experiments using video data of 25 baseball games. While the recall rate with video mode only was 82.8% and that with audio mode only was 86.6%, the proposed method achieved 90.4%. This method was proved to be significantly effective to reduce the cost for making highlight for baseball video content.