Image Matching via Feature Fusion and Coherent Constraint

The Gaussian mixture model (GMM)-based methods have achieved great success in point set registration. However, they cannot be directly applied to image matching, because the features extracted from two images usually contain a large portion of outliers. In this letter, we propose a new method to extend the powerful GMM to the field of image feature points matching. The algorithm consists of two main steps. In the first step, points extracted from the images are mapped into a new subspace, in which feature similarity information is fused to get the new representation of the points. The second step performs an improved progressive process with the GMM to find correspondences satisfying the coherent constraint. In this way, finding correspondences among large outliers is feasible and the iteration converges faster. Experimental results on benchmark data sets show that the proposed method can find more correct matches with high accuracy.