Probabilistic relevance feedback approach for content-based image retrieval based on gaussian mixture models

Abstract

A new relevance feedback (RF) approach for content-based image retrieval is presented. This approach uses Gaussian mixture (GM) models of the image features and a query that is updated in a probabilistic manner. This update reflects the preferences of the user and is based on the models of both the positive and negative feedback images. The retrieval is based on a recently proposed distance measure between probability density functions, which can be computed in closed form for GM models. The proposed approach takes advantage of the form of this distance measure and updates it very efficiently based on the models of the user-specified relevant and irrelevant images. It is also shown that this RF framework is fairly general and can be applied in case other image models or distance measures are used instead of those proposed in this work. Finally, comparative numerical experiments are provided, which that demonstrate the merits of the proposed RF methodology and the use of the distance measure, and also the advantages of using GMs for image modelling.