Robust highlight extraction using multi-stream hidden Markov models for baseball video
Bach, N.H.
Shinoda, K.
Furui, S.
Dept. of Comput. Sci., Tokyo Inst. of Technol., Japan;
This paper appears in: Image Processing, 2005. ICIP 2005. IEEE International Conference on
Publication Date: 11-14 Sept. 2005
Volume: 3,
On page(s): III- 173-6
ISBN: 0-7803-9134-9
INSPEC Accession Number: 8835848
Digital Object Identifier: 10.1109/ICIP.2005.1530356
Current Version Published: 2006-03-27
Abstract
This paper proposes a robust statistical framework to extract highlights from a baseball broadcast video. We applied multi-stream hidden Markov models (HMMs) to control the weights among different features. To achieve robustness against new highlights, we used a common simple structure for all the HMMs. In addition, scene segmentation and unsupervised adaptation were applied to achieve more robustness against the differences of environmental conditions among games. The precision rate of high-light extracting experiments for eight kinds of highlights from 4.5 hours of digest data was 77.4% and was increased to 78.7% by applying scene segmentation. Furthermore, the unsupervised adaptation method improved precision by 2.7 points to 81.4%. These results confirm the effectiveness of our framework.
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