HMM model selection issues for soccer video

Baillie, M. and Jose, J.M. and van Rijsbergen, C.J.; (2004) HMM model selection issues for soccer video. In: Lecture Notes in Computer Science. Springer. ISBN 0302-9743

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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.

Creators(s): Baillie, M., Jose, J.M. and van Rijsbergen, C.J.;
Item type:Book Section
ID code:2730
Keywords:information retrieval, video retrieval, learning objects, hidden Markov model, classification, sport, Electronic computers. Computer science
Subjects:Science > Mathematics > Electronic computers. Computer science
Department:Faculty of Science > Computer and Information Sciences
Depositing user: Strathprints Administrator
Date deposited:28 Mar 2007
Last modified:20 Jan 2021 15:10
URI:https://strathprints.strath.ac.uk/id/eprint/2730
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