Effective and efficient sports highlights extraction using the minimum description length criterion in selecting GMM structures

Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In fitting the training data with Guassian Mixture Models(GMMs) of appropriate structures using the MDL 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 high-lights. Since we have focused on audio domain processing, it enables us to extract highlights very fast. In this paper, we have demonstrated the importance of a better understanding of model structures in such a pattern recognition task.

Original languageEnglish (US)
Title of host publication2004 IEEE International Conference on Multimedia and Expo (ICME)
Pages1947-1950
Number of pages4
StatePublished - Dec 1 2004
Event2004 IEEE International Conference on Multimedia and Expo (ICME) - Taipei, Taiwan, Province of China
Duration: Jun 27 2004Jun 30 2004

Publication series

Name2004 IEEE International Conference on Multimedia and Expo (ICME)
Volume3

Other

Other2004 IEEE International Conference on Multimedia and Expo (ICME)
Country/TerritoryTaiwan, Province of China
CityTaipei
Period6/27/046/30/04

Keywords

  • Gaussian Mixture Models
  • Minimum Description Length
  • Model Structure
  • Sports Highlights Extraction

ASJC Scopus subject areas

  • Engineering(all)

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