Support vector machine meta classifier based sport video classification

Long Fei Zhang*, Yuan Da Cao, Yi Hua Zhou, Jian Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

A support vector machine (SVM) meta classifier based sport video classification algorithm is presented to bridge the low level feature and high level semantic feature. Domain knowledge rules are exploited to extract features semantically. Meta classifiers classify the video clips with combination strategies. The experimental results showed that the algorithm can be used in almost all sports video classification, and have better performance than other non-semantic associate classification algorithms with an accuracy attaining 92.23%.

Original languageEnglish
Pages (from-to)41-44+67
JournalBeijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume26
Issue number1
Publication statusPublished - Jan 2006

Keywords

  • Domain knowledge rules
  • Meta classifier
  • Sports video classification
  • Support vector machine
  • Video classification

Fingerprint

Dive into the research topics of 'Support vector machine meta classifier based sport video classification'. Together they form a unique fingerprint.

Cite this