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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hmm_soccer.pdf 171kB |
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 |
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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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