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Video Summarization Using Hidden Markov Model  

박호식 (관동대학교)
배철수 (관동대학교)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.8, no.6, 2004 , pp. 1175-1181 More about this Journal
Abstract
This paper proposes a system to analyze and summarize the video shots of baseball game TV program into fifteen categories. Our System consists of three modules: feature extraction, Hidden Markov Model (HMM) training, and video shot categorization. Video Shots belongs to the same class are not necessarily similar, so we require that the training set is large enough to include video shot with all possible variations to create a robust Hidden Markov Model. In the experiments, we have illustrated that our system can recognize the 15 different shot classes with a success ratio of 84.72%.
Keywords
Feature Extraction; Hidden Markov Model; Video Shot categorization;
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