Using decision-tree to automatically construct learned-heuristics for events classification in sports video
Tjondronegoro, Dian and Chen, Yi-Ping Phoebe 2006, Using decision-tree to automatically construct learned-heuristics for events classification in sports video, in Proceedings of the 2006 IEEE International Conference on Multimedia and Expo, Institute of Electrical and Electronics Engineers, Inc., Piscataway, N.J., pp. 1465-1468.
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Proceedings of the 2006 IEEE International Conference on Multimedia and Expo
Guan, Ling Zhang, Hong-Jiang
International Conference on Multimedia and Expo
Institute of Electrical and Electronics Engineers, Inc.
Place of publication
Automatic events classification is an essential requirement for constructing an effective sports video summary. It has become a well-known theory that the high-level semantics in sport video can be “computationally interpreted” based on the occurrences of specific audio and visual features which can be extracted automatically. State-of-the-art solutions for features-based event classification have only relied on either manual-knowledge based heuristics or machine learning. To bridge the gaps, we have successfully combined the two approaches by using learning-based heuristics. The heuristics are constructed automatically using decision tree while manual supervision is only required to check the features and highlight contained in each training segment. Thus, fully automated construction of classification system for sports video events has been achieved. A comprehensive experiment on 10 hours video dataset, with five full-match soccer and five full-match basketball videos, has demonstrated the effectiveness/robustness of our algorithms.
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