TY - GEN
T1 - Image retrieval with embedded sub-class information using Gaussian mixture models
AU - Muneesawang, P.
AU - Guan, L.
N1 - Publisher Copyright:
© 2003 IEEE.
PY - 2003
Y1 - 2003
N2 - This paper describes content-based image retrieval techniques within the relevance feedback framework. The Gaussian mixture model (GMM) is used to characterize sub-class information to increase retrieval accuracy and reduce number of interactions during a query session. The implementation of GMM is based on the radial basis function using a new learning algorithm that can cope with small training samples in the relevance feedback cycle. The proposed retrieval system is successfully applied to image databases of very large sizes, and experimental results show that the proposed system competes favorably with the other recently proposed interactive systems.
AB - This paper describes content-based image retrieval techniques within the relevance feedback framework. The Gaussian mixture model (GMM) is used to characterize sub-class information to increase retrieval accuracy and reduce number of interactions during a query session. The implementation of GMM is based on the radial basis function using a new learning algorithm that can cope with small training samples in the relevance feedback cycle. The proposed retrieval system is successfully applied to image databases of very large sizes, and experimental results show that the proposed system competes favorably with the other recently proposed interactive systems.
UR - https://www.scopus.com/pages/publications/33750106574
U2 - 10.1109/ICME.2003.1221031
DO - 10.1109/ICME.2003.1221031
M3 - Conference contribution
AN - SCOPUS:33750106574
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
SP - I769-I772
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 -