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Abstract

In this paper we propose a simple way of significantly improving the performance of the Softassign graph-matching algorithm of Gold and Rangarajan. Exploiting recent theoretical results in spectral graph theory we use diffusion kernels to transform a matching problem between unweighted graphs into a matching between weighted ones in which the weights rely on the entropies of the probability distributions associated to the vertices after kernel computation. In our experiments, we report that weighting the original quadratic cost function results in a notable improvement of the matching performance, even in medium and high noise conditions.

Keywords

Edge Density Graph Match Quadratic Cost Function Unweighted Graph Graph Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Miguel Angel Lozano
    • 1
  • Francisco Escolano
    • 1
  1. 1.Robot Vision Group, Departamento de Ciencia de la Computación e Inteligencia ArtificialUniversidad de AlicanteSpain

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