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Extraction and classification of self-consumable sport video highlights using generic HMM

Tjondronegoro, Dian, Chen, Yi-Ping Phoebe and Pham, Binh 2005, Extraction and classification of self-consumable sport video highlights using generic HMM, in Contested role for perspective in three-dimensional information visualisation : Proceedings of the 4th Asia Pacific International Symposium on Information Technology, IEEE Computer Society Press, Los Alamitos, Calif., pp. 200-203.

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Title Extraction and classification of self-consumable sport video highlights using generic HMM
Author(s) Tjondronegoro, Dian
Chen, Yi-Ping Phoebe
Pham, Binh
Conference name Asia Pacific International Symposium on Information Technology (4th : 2005 : Gold Coast, Qld.)
Conference location Gold Coast, Qld.
Conference dates 26-27 January 2005
Title of proceedings Contested role for perspective in three-dimensional information visualisation : Proceedings of the 4th Asia Pacific International Symposium on Information Technology
Editor(s) Rhee, S. B.
Publication date 2005
Conference series Asia Pacific International Symposium on Information Technology
Start page 200
End page 203
Publisher IEEE Computer Society Press
Place of publication Los Alamitos, Calif.
Keyword(s) self-consumable highlights
sport video summarization
Hidden Markov Model (HMM)
audiovisual features
Summary This paper aims to automatically extract and classify self-consumable sport video highlights. For this purpose, we will emphasize the benefits of using play-break sequences as the effective inputs for HMMbased classifier. HMM is used to model the stochastic pattern of high-level states during specific sport highlights which correspond to the sequence of generic audio-visual measurements extracted from raw video data. This paper uses soccer as the domain study, focusing on the extraction and classification of goal, shot and foul highlights. The experiment work which uses183 play-break sequences from 6 soccer matches will be presented to demonstrate the performance of our proposed scheme.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 1920952284
9781920952280
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2005, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30009701

Document type: Conference Paper
Collections: School of Information Technology
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