On the choice of similarity measures for image retrieval by example

In image retrieval systems, a variety of simple similarity measures are used. The choice for one similarity measure or another is generally driven by an experimental comparison on a labeled database. The drawback of such an approach is that, while a large number of possible similarity measures can be tested, we do not know how to extend from the obtained results. However, the choice of a good similarity measure leads to noticeable better results. It is known that this choice is related to the variability of the images within the same class. Therefore, we propose a model of image retrieval systems and deduce a scheme for deriving the best similarity measure in a set of similarity measures, assuming a parametric model of the variability of feature vectors within the same class. An experimental validation of the model and the derived similarity measures is performed on synthetic ground-truth databases. Finally, from our experiments, we give several rules to follow for the design of ground-truth databases allowing reliable conclusions on the search of better similarity measures.