DocumentCode
2576676
Title
Effective and efficient sports highlights extraction using the minimum description length criterion in selecting GMM structures [audio classification]
Author
Xiong, Ziyou ; Radhakrishnan, Rathnakumar ; Divakaran, Ajay ; Huang, Thomas S.
Author_Institution
Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL, USA
Volume
3
fYear
2004
fDate
27-30 June 2004
Firstpage
1947
Abstract
In fitting the training data with Gaussian mixture models (GMMs) of appropriate structures using the MDL (minimum description length) criterion, we are able to improve audio classification accuracy with a large margin. With the MDL-GMMs, we are also able to greatly improve the accuracy in extracting sports highlights. Since we have focused on audio domain processing, it enables us to extract highlights very quickly. We have demonstrated the importance of a better understanding of model structures in such a pattern recognition task.
Keywords
Gaussian distribution; audio signal processing; classification; feature extraction; multimedia computing; GMM structures; Gaussian mixture models; MDL-GMM; audio classification accuracy; audio domain processing; minimum description length estimator; pattern recognition; sports highlights extraction; training data fitting; video analysis; Clustering algorithms; Data engineering; Data mining; Electronic mail; Feature extraction; Laboratories; Maximum likelihood estimation; Parameter estimation; Pattern recognition; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Multimedia and Expo, 2004. ICME '04. 2004 IEEE International Conference on
Print_ISBN
0-7803-8603-5
Type
conf
DOI
10.1109/ICME.2004.1394642
Filename
1394642
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