Learning combined similarity measures from user data for image retrieval

Image retrieval has become an interesting and active field due to the increasing necessity of searching and browsing very large image repositories. Images are represented using several kinds of low-level descriptors from which convenient similarity or score functions are computed. Recent work deals with different ways of combining these measures to improve the overall performance of the retrieval system. This paper builds upon previous ideas taken from different contexts to deploy a convenient combination framework that takes into account learning data directly gathered from the users that are supposed to end using the system. The proposal is empirically evaluated and compared to other ways of combining the same measures.