A content-based image retrieval scheme allowing for robust automatic personalization

Abstract

The retrieval performance of content-based image retrieval (CBIR) systems is often disappointingly low, mainly due to the subjectivity of human perception. Relevance feedback (RF) has been widely considered as a powerful tool to enhance CBIR systems by incorporating human perception subjectivity into the retrieval procedure. However, usually, the obtained feedback logs are scarce and contain lots of outliers, undermining the RF adaptation effectiveness. In this paper, we tackle these shortcomings exploiting the inherent outlier downweighting capabilities mixtures of Student's t distributions offer. Each semantic class is modeled by a mixture of t distributions fitted to data provided by the system operators. Further, the semantic class models get personalized by application of a novel, efficient RF algorithm allowing for the robust adaptation of the semantic class models to the accumulated feedback of each user. The efficacy of our approach is validated through a series of experiments using objective performance criteria