Unified graph matching in Euclidean spaces

Graph matching is a classical problem in pattern recognition with many applications, particularly when the graphs are embedded in Euclidean spaces, as is often the case for computer vision. There are several variants of the matching problem, concerned with isometries, isomorphisms, homeomorphisms, and node attributes; different approaches exist for each variant. We show how structured estimation methods from machine learning can be used to combine such variants into a single version of graph matching. In this paradigm, the extent to which our datasets reveal isometries, isomorphisms, homeomorphisms, and other properties is automatically accounted for in the learning process so that any such specific qualification of graph matching loses meaning. We present experiments with real computer vision data showing the leverage of this unified formulation.