A UIM/ICM based approach to content-based image retrieval

This paper presents a new similarity measure and matching scheme for content-based image retrieval (CBIR), based on modeling positive and negative hypotheses and testing a query image against these two hypotheses. The paper proposes to calculate first a universal image model (UIM), which is built based on a large set of images. The derived UIM is then used as a reference for the calculation of adapted models for each image class, which is done by a Bayesian adaptation of the GMM. The image class models (ICM) are therefore based on adapted versions of the background mixture components. Querying is based on the likelihood ratio between the values of these two hypotheses. A parameter adaptation technique is also introduced based on the background hypothesis. In addition, the paper discussed an acceleration technique based on ranking the closest Gaussian components of the background model and using their corresponding components in the positive classes. The experimental results show that the proposed approach improves the robust and evident performance.