HMM model selection issues for soccer video

M. Baillie, J.M. Jose, C.J. van Rijsbergen

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
ISBN (Print)0302-9743
Publication statusPublished - 2004


  • information retrieval
  • video retrieval
  • learning objects
  • hidden Markov model
  • classification
  • sport


Dive into the research topics of 'HMM model selection issues for soccer video'. Together they form a unique fingerprint.

Cite this