HMM model selection issues for Soccer video

Baillie, M., Jose, J.M. and Van Rijsbergen, C.J. (2004) HMM model selection issues for Soccer video. Lecture Notes in Computer Science, 3115, pp. 70-78. (doi:10.1007/b98923)

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Publisher's URL: http://dx.doi.org/10.1007/b98923

Abstract

There has been a concerted effort from the video retrieval community to develop tools that automate the annotation process of Sports video. In this paper, we provide an in-depth investigation into three Hidden Markov Model (HMM) selection approaches. Where HMM, a popular indexing framework, is often applied in a ad hoc manner. We investigate what effect, if any, poor HMM selection can have on future indexing performance when classifying specific audio content. Audio is a rich source of information that can provide an effective alternative to high dimensional visual or motion based features. As a case study, we also illustrate how a superior HMM framework optimised using a Bayesian HMM selection strategy, can both segment and then classify Soccer video, yielding promising results.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon and Van Rijsbergen, Professor Cornelis
Authors: Baillie, M., Jose, J.M., and Van Rijsbergen, C.J.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Publisher:Springer
ISSN:0302-9743
Copyright Holders:Copyright © Springer 2004
First Published:First published in Lecture Notes in Computer Science 3115:70-78
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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