Abstract: One of the most fundamental problems in computer vision is to evaluate the similarity between images of objects. Although the problem has attracted a lot of research efforts by computer scientists and psychologists, it does not have at present a satisfactory solution which is comparable to humans' abilities. We developed a generic measure for images similarity based on the combination of overlapping patches. We present a method to combine several sources of similarity assessments into a single score, and derive an algorithm that is robust to small deformations of parts in various positions and scales. Our relative dynamic programming algorithm is a variant of dynamic programming that is not inherently one-dimensional, and its scores are on a relative scale.

Joint work with Shimon Ullman.