Towards Bridging the Gap between Statistical and Structural Pattern Recognition: Two New Concepts in Graph Matching

  • H. Bunke
  • S. Günter
  • X. Jiang
Conference paper

DOI: 10.1007/3-540-44732-6_1

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2013)
Cite this paper as:
Bunke H., Günter S., Jiang X. (2001) Towards Bridging the Gap between Statistical and Structural Pattern Recognition: Two New Concepts in Graph Matching. In: Singh S., Murshed N., Kropatsch W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg

Abstract

Two novel concepts in structural pattern recognition are discussed in this paper. The first, median of a set of graphs, can be used to characterize a set of graphs by just a single prototype. Such a characterization is needed in various tasks, for example, in clustering. The second novel concept is weighted mean of a pair of graphs. It can be used to synthesize a graph that has a specified degree of similarity, or distance, to each of a pair of given graphs. Such an operation is needed in many machine learning tasks. It is argued that with these new concepts various well-established techniques from statistical pattern recognition become applicable in the structural domain, particularly to graph representations. Concrete examples include k-means clustering, vector quantization, and Kohonen maps.

Keywords

Graph matching error-tolerant matching edit distance median graph weighted mean 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • H. Bunke
    • 1
  • S. Günter
    • 1
  • X. Jiang
    • 1
  1. 1.Dept. of Computer ScienceUniv. of BernSwitzerland

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