Region-based image indexing and retrieval inspired by text search

In this paper we present an approach for image similarity search that takes inspiration from text retrieval. Images are indexed using visual terms chosen from a visual lexicon. Each visual term represents a typology of visual regions, according to various criteria. The visual lexicon is obtained by analyzing a training set of images, to infer which are the relevant typology of visual regions. We have defined a weighting and matching schema that are able respectively to associate visual terms with images and to compare images by means of the associated terms. We show that the proposed approach do not lose performance, in terms of effectiveness, with respect to other methods existing in literature, and at the same time offers higher performance, in terms of efficiency, given the possibility of using inverted files to support similarity searching. The proposed techniques were implemented in a running prototype.