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Graph Matching Using Manifold Embedding

  • Bai Xiao
  • Hang Yu
  • Edwin Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

This paper describes how graph-spectral methods can be used to transform the node correspondence problem into one of point-set alignment. We commence by using a heat kernel analysis to compute geodesic distances between nodes in the graphs. With geodesic distances to hand, we use the ISOMAP algorithm to embed the nodes of a graph in a low-dimensional Euclidean space. With the nodes in the graph transformed to points in a metric space, we can recast the problem of graph-matching into that of aligning the points. Here we use a variant of the Scott and Longuet-Higgins algorithm to find point correspondences. We experiment with the resulting algorithm on a number of real-world problems.

Keywords

Heat Kernel Geodesic Distance Graph Match Spectral Graph Theory Embed Point 
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

  • Bai Xiao
    • 1
  • Hang Yu
    • 1
  • Edwin Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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