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Graph Matching Using Spectral Seriation

  • Conference paper
Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2683))

Abstract

This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that it lacks the formality and rigour of string edit distance computation. Hence, our aim is to convert graphs to string sequences so that standard string edit distance techniques can be used. To do this, we use a graph spectral seriation method to convert the adjacency matrix into a string or sequence order. We pose the problem of graph-matching as maximum a posteriori probability alignment of the seriation sequences for pairs of graphs. This treatment leads to an expression for the edit costs. We compute the edit distance by finding the sequence of string edit operations which minimise the cost of the path traversing the edit lattice. The edit costs are defined in terms of the a posteriori probability of visiting a site on the lattice. We demonstrate the method with results on a data-set of Delaunay graphs.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of York, York, Y01 5DD, UK

    Antonio Robles-Kelly & Edwin R. Hancock

Authors
  1. Antonio Robles-Kelly
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  2. Edwin R. Hancock
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Editor information

Editors and Affiliations

  1. Department of CISE, University of Florida, Gainesville, FL, USA

    Anand Rangarajan

  2. Instituto de Telecomunicações, Instituto Superior Técnico, T.U. Lisbon, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Mário Figueiredo

  3. ARIANA Research Group, INRIA/I3S, Sophia Antipolis, France

    Josiane Zerubia

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© 2003 Springer-Verlag Berlin Heidelberg

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Robles-Kelly, A., Hancock, E.R. (2003). Graph Matching Using Spectral Seriation. In: Rangarajan, A., Figueiredo, M., Zerubia, J. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2003. Lecture Notes in Computer Science, vol 2683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45063-4_33

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  • DOI: https://doi.org/10.1007/978-3-540-45063-4_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40498-9

  • Online ISBN: 978-3-540-45063-4

  • eBook Packages: Springer Book Archive

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