Image matching using adapted image models and its application to content-based image retrieval

The image matching is an important and necessary step in a series of processes aimed at overall content-based image retrieval (CBIR). This paper presents a new image matching approach for CBIR. It first defines the concept of image-class and the process of image matching in CBIR is formulated into a set of hypothesis tests. The retrieval is based on modeling positive and negative hypotheses and testing a query image against these two hypotheses. In order to model the two hypotheses, the paper proposes to calculate first a universal image model (UIM). The derived UIM is then used as a reference for the calculation of adapted models for each image in the database. The use of Bayesian adaptation to derive well-behaved image models from an UIM in image retrieval is a main contribution of the paper. In addition, the paper discusses an acceleration technique based on ranking the closest mixture Gaussian components of the background model and using their corresponding components in the positive classes. The experimental results on publicly available datasets show that the proposed approach improves the robust and evident performance.