TY - GEN
T1 - Sports video classification using HMMS
AU - Gibert, X.
AU - Li, Huiping
AU - Doermann, D.
N1 - Publisher Copyright:
© 2003 IEEE.
PY - 2003
Y1 - 2003
N2 - In this paper we address the problem of sports video classification using hidden Markov models (HMMs). For each sports genre, we construct two HMMs representing motion and color features respectively. The observation sequences generated from the principal motion direction and the principal color of each frame are fed to a motion and a color HMM respectively. The outputs are integrated to make a final decision. We tested our scheme on 220 minutes of sports video with four genre types: ice hockey, basketball, football, and soccer, and achieved an overall classification accuracy of 93%.
AB - In this paper we address the problem of sports video classification using hidden Markov models (HMMs). For each sports genre, we construct two HMMs representing motion and color features respectively. The observation sequences generated from the principal motion direction and the principal color of each frame are fed to a motion and a color HMM respectively. The outputs are integrated to make a final decision. We tested our scheme on 220 minutes of sports video with four genre types: ice hockey, basketball, football, and soccer, and achieved an overall classification accuracy of 93%.
UR - https://www.scopus.com/pages/publications/84908567218
U2 - 10.1109/ICME.2003.1221624
DO - 10.1109/ICME.2003.1221624
M3 - Conference contribution
AN - SCOPUS:84908567218
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
SP - II345-II348
BT - Proceedings - 2003 International Conference on Multimedia and Expo, ICME
PB - IEEE Computer Society
T2 - 2003 International Conference on Multimedia and Expo, ICME 2003
Y2 - 6 July 2003 through 9 July 2003
ER -