Bayesian relevance feedback for content-based image retrieval

Despite the efforts to reduce the so-called semantic gap between the user's perception of image similarity and the 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, the Bayesian decision theory is used to estimate the boundary between relevant and non-relevant images. Then, a new query is computed whose neighbourhood is likely to fall in a region of the feature space containing relevant images. The performances of the proposed query shifting method have been compared with those of other relevance feedback mechanisms described in the literature. Reported results show the superiority of the proposed method.