An approximate algorithm for structural matching of images

We provide a novel strategy to structurally match two images. The matching process takes into account minor structural variations between the two images. Given two images, we first construct random parametric structural descriptions (RPSDs) to capture their acceptable variations. A RPSD induces a joint probability distribution over a hypergraph that captures the image structure using image primitives and the relations among them. The RSPDs from the two images are matched using an information theoretic entropy measure which quantify the similarity of the joint structural probability distributions modeled by the RPSDs. The computation of this entropic similarity measure requires the matching of the hypergraphs underlying the RPSDs which, as we know, is NP-hard. So, we propose an approximate strategy which finds the solution by generating a sequence of possible mapping functions between the primitives from the two images. We present experimental validation of the strategy on a variety of images