A new axiomatic methodology for the image similarity

In this paper, a new theoretical framework is introduced which explores the notion of image similarity in two axiomatic conditions. The first one resembles to the concept of frequency in matching image patches. The second one formalizes the high-level strategy for computing the image similarity. A realization of this theoretical framework is a new image similarity measure based on approximate matching of patches shared between the images. The approximate match is defined in terms of normalized 2D Hamming distance, and verified within a given neighbourhood from the position of the patches in the images. The similarity measure is computed as the average area of these shared patches. The proposed approach is tested on well-known benchmark datasets. The obtained results show that the proposed method overcomes, in terms of retrieval precision and computational complexity, other competing measures adopted in image retrieval.