A Unified Probabilistic Approach to Feature Matching and Object Segmentation

This paper deals with feature matching and segmentation of common objects in a pair of images, simultaneously. For the feature matching problem, the matching likelihoods of all feature correspondences are obtained by combining their discriminative power with the spatial coherence constraint that favors their spatial aggregation via object segmentation. At the same time, for the object segmentation problem, our algorithm estimates the object likelihood that each subregion is a commonly existing part in two images by the affinity propagation of the resulted matching likelihoods. Since these two problems are related to each other, our main idea to solve them is to integrate all the priors about them into a unified framework, that consists of several correlated quadratic cost functions. Eventually, all matching and object likelihoods are estimated simultaneously as a solution of linear system of equations. Based on these likelihoods, we finally recover the optimal feature matches and the common object parts by imposing simple sequential mapping and thresholding techniques, respectively. The experiments demonstrate the superiority of our algorithm compared with the conventional methods.