PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Graph simplification and matching using commute times
Huaijun Qiu and Edwin Hancock
Pattern Recognition Volume 40, Number 10, pp. 2874-2889, 2007. ISSN 0031-3203


This paper exploits the properties of the commute time for the purposes of graph simplification and matching. Our starting point is the lazy random walk on the graph, which is determined by the heat kernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterise the random walk using the commute time between nodes, and show how this quantity may be computed from the Laplacian spectrum using the discrete Green's function. In this paper, we explore two different, but essentially dual, simplified graph representations delivered by the commute time. The first representation decomposes graphs into concentric layers. To do this we augment the graph with an auxiliary node which acts as a heat source. We use the pattern of commute times from this node to decompose the graph into a sequence of layers. Our second representation is based on the minimum spanning tree of the commute time matrix. The spanning trees located using commute time prove to be stable to structural variations. We match the graphs by applying a tree-matching method to the spanning trees. We experiment with the method on synthetic and real-world image data, where it proves to be effective.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Theory & Algorithms
ID Code:3888
Deposited By:Edwin Hancock
Deposited On:25 February 2008