LAUSR

Graph matching and classification play fundamental roles in computer vision. The computational complexity of the conventional method based on a spectrum method is high, which prevents it from handling large graphs in practice. This work proposes a novel framework for tackling the challenge by using conformal module. We apply the classical Hodge theory from differential manifold to the graph setting and compute the combinatorial conformal invariant of the graph, called as conformal module, which can be used as the fingerprint for the graph. The method is applicable for viewpoint classification and posture detection. The experimental results demonstrate the efficiency and efficacy of the proposed method.