Local invariant feature extraction methods are widely used for image-features matching. There exist a number of approaches aimed at the refinement of the matches between image-features. It is a common strategy among these approaches to use geometrical criteria to reject a subset of outliers. One limitation of the outlier rejection design is that it is unable to add new useful matches. We present a new model that integrates the local information of the SIFT descriptors along with global geometrical information to estimate a new robust set of feature-matches. Our approach encodes the geometrical information by means of graph structures while posing the estimation of the feature-matches as a graph matching problem. Some comparative experimental results are presented.

Peer Reviewed