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.
Original language | English |
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Title of host publication | Lecture Notes in Computer Science |
Publisher | Springer |
ISBN (Print) | 0302-9743 |
DOIs | |
Publication status | Published - 2004 |
Keywords
- information retrieval
- video retrieval
- learning objects
- hidden Markov model
- classification
- sport