Relevance Feedback Content-Based Image Retrieval Using Query Distribution Estimation Based on Maximum Entropy Principle

In the last few years we have seen an upsurge of interest in contentbased image retrieval CBIR the selection of images from a collection via fea tures extracted from images themselves Typically the nearestneighbor rule is used to retrieve images from a query image However the underlying query distribution may not be isotropic in nature Hence a more sophisticated estimation for the query dis tribution is required We propose a novel relevance feedback framework for image retrieval which con tains two stages to estimate the query distri bution based on relevance feedback information and to generate a set of inquiries for relevance selec tion based on the MaximumEntropy Principle W