A novel dynamic multi-model relevance feedback procedure for content-based image retrieval

This paper deals with the problem of image retrieval in large databases with a big semantic gap by a relevance feedback procedure. We present a novel algorithm for modelling the users's preferences in the content-based image retrieval system.The proposed algorithm considers the probability of an image belonging to the set of those sought by the user, and estimates the parameters of several local logistic regression models whose inputs are the low-level image features. A Principal Component Analysis method is applied to the original vector to reduce its high dimensionality. The relevance probabilities predicted by these local models are combined by means of a weighted average. These weights are obtained according to the variance explained by the group of principal components used for each local model. These models are dynamically estimated in each iteration of the relevance feedback algorithm until the user is satisfied.This novel procedure has been tested in a collection with a large semantic gap, the Wikipedia collection. Two types of experiments have been performed, one with an automatic user and another with a typical user. The method is compared to some recent similar approaches in literature, obtaining very good performance in terms of the MAP evaluation measure.