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Graph Matching – Challenges and Potential Solutions

  • Horst Bunke
  • Christophe Irniger
  • Michel Neuhaus
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

Structural pattern representations, especially graphs, have advantages over feature vectors. However, they also suffer from a number of disadvantages, for example, their high computational complexity. Moreover, we observe that in the field of statistical pattern recognition a number of powerful concepts emerged recently that have no equivalent counterpart in the domain of structural pattern recognition yet. Examples include multiple classifier systems and kernel methods. In this paper, we survey a number of recent developments that may be suitable to overcome some of the current limitations of graph based representations in pattern recognition.

Keywords

structural pattern recognition graph matching graph edit distance automatic learning of cost functions graph kernel methods multiple classifier systems graph database retrieval 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Horst Bunke
    • 1
  • Christophe Irniger
    • 1
  • Michel Neuhaus
    • 1
  1. 1.Institute of Computer Science and Applied MathematicsUniversity of BernBernSwitzerland

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