On stability of adaptive similarity measures for content-based image retrieval

Abstract

Retrieving similar images is a challenging task for today's content-based retrieval systems. Aiming at high retrieval performance, these systems frequently capture the user's notion of similarity through expressive image models and adaptive similarity measures, which try to approximate the individual user-dependent notion of similarity as close as possible. As image models appearing on the query side can significantly differ in quality compared to those stored in the multimedia database, similarity measures have to be robust against these individual quality changes in order to maintain high retrieval performance. In order to evaluate the robustness of similarity measures, we introduce the general concept of the stability of a similarity measure with respect to query modifying transformations describing the change in quality on the query side. In addition, we include a comparison of the stability of the major state-of-the-art adaptive similarity measures based on different benchmark image databases. © 2012 Springer-Verlag.