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Generation of sports highlights using a combination of supervised & unsupervised learning in audio domain

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4 Author(s)
Regunathan Radhakrishan ; Mitsubishi Electr. Res. Labs., Cambridge, MA, USA ; Ziyou Xiong ; Divakaran, A. ; Ishikawa, Y.

In our past work we have used supervised audio classification to develop a common audio-based platform for highlight extraction that works across three different sports. We then use a heuristic to post-process the classification results to identify interesting events and also to adjust the summary length. In this paper, we propose a combination of unsupervised and supervised learning approaches to replace the heuristic. The proposed unsupervised framework mines the semantic audio-visual labels so as to detect "interesting" events. We then use a hidden Markov model based approach to control the length of the summary. Our experimental results show that the proposed techniques are promising.

Published in:

Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on  (Volume:2 )

Date of Conference:

15-18 Dec. 2003