A multimodal data mining framework for soccer goal detection based on decision tree logic

  • Authors:

  • Shu-Ching Chen;Mei-Ling Shyu;Chengcui Zhang;Min Chen

  • Affiliations:

  • Distributed Multimedia Information System Laboratory, School of Computer Science, Florida International University, Miami, FL 33199, USA.;Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124, USA.;Department of Computer and Information Science, University of Alabama at Birmingham, Birmingham, AL 35294, USA.;Distributed Multimedia Information System Laboratory, School of Computer Science, Florida International University, Miami, FL 33199, USA

  • Venue:
  • International Journal of Computer Applications in Technology
  • Year:
  • 2006

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Abstract

In this paper, we propose a new multimedia data mining framework for the extraction of soccer goal events in soccer videos by utilising both multimodal analysis and decision tree logic. The extracted events can be used to index the soccer videos. We first adopt an advanced video shot detection method to produce shot boundaries and some important visual features. Then, the visual/audio features are extracted for each shot at different granularities. This rich multimodal feature set is then filtered by a pre-filtering step in order to clean the noise as well as to reduce the irrelevant data. A decision tree model is built upon the cleaned data set and is used to classify the goal shots. We also present the experimental results for the proposed framework, which indicate the performance of the framework for soccer goal extraction.