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Using decision-tree to automatically construct learned-heuristics for events classification in sports video

Tjondronegoro, Dian W. & Chen, Yi-Ping Phoebe (2006) Using decision-tree to automatically construct learned-heuristics for events classification in sports video. In Guan, Ling & Zhang, Hong Jiang (Eds.) IEEE International Conference on Multimedia and Expo (ICME) 2006, 9-12 July 2006, Toronto, Canada.

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Abstract

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.

Impact and interest:

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ID Code: 4968
Item Type: Conference Paper
Keywords: sports video, events classification, decision tree, learned heuristics
DOI: 10.1109/ICME.2006.262818
ISBN: 1424403677
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTER SOFTWARE (080300) > Multimedia Programming (080305)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Information Systems
Copyright Owner: Copyright 2006 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 08 Sep 2006
Last Modified: 29 Feb 2012 23:30

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