Query Shifting Based on Bayesian Decision Theory for Content-Based Image Retrieval

Despite the efforts to reduce the so-called semantic gap between the user’s perception of image similarity and feature-based representation of images, the interaction with the user remains fundamental to improve performances of content-based image retrieval systems. To this end, relevance feedback mechanisms are adopted to refine image-based queries by asking users to mark the set of images retrieved in a neighbourhood of the query as being relevant or not. In this paper, Bayesian decision theory is used to compute a new query whose neighbourhood is more likely to fall in a region of the feature space containing relevant images. The proposed query shifting method outperforms two relevance feedback mechanisms described in the literature. Reported experiments also show that retrieval performances are less sensitive to the choice of a particular similarity metric when relevance feedback is used