Improving the retrieval performance of content-based image retrieval systems: the GIVBAC approach

Abstract

The retrieval performance of content-based image retrieval (CBIR) systems still leaves much to be desired, especially when the system is serving as an interface to an image collection covering many different topics. The problem of missing semantical information about the images leads to great numbers of false matches because of misleading similarities in the visual primitives that are retrieved. This paper introduces an approach called GIVBAC, that tries to reduce the number of false matches in query result sets. It relies heavily on user feedback on retrieval results with regard to user-definable thematic groups. Feedback is used globally, i.e. the feedback of one user has influences on the behaviour of the whole system. The weight of individual votes is a parameter of GIVBAC so that it can be adjusted to the level of trust the user base is given, i.e. higher values in a closed user group and lower values in an open group with anonymous users. The paper presents results of an evaluation comparing the performance of an unmodified CBIR system with a system that is modified so that it uses GIVBAC as an interface to the users.