Image retrieval with embedded sub-class information using Gaussian mixture models

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.