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

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Advances in Pattern Recognition — ICAPR 2001 (ICAPR 2001)

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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.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Univ. of Bern, Switzerland

    H. Bunke, S. Günter & X. Jiang

Authors
  1. H. Bunke
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  2. S. Günter
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  3. X. Jiang
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Exeter, EX4 4PT, Exeter, UK

    Sameer Singh

  2. Computational Intelligence Group, Tuiuti University of Parana, Curitiba, Brazil

    Nabeel Murshed

  3. Institute of Computer Aided Automation PRIP-Group 1832, Vienna University of Technology, Favoritenstr. 9/2/4, 1040, Wien, Austria

    Walter Kropatsch

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© 2001 Springer-Verlag Berlin Heidelberg

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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. https://doi.org/10.1007/3-540-44732-6_1

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  • DOI: https://doi.org/10.1007/3-540-44732-6_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41767-5

  • Online ISBN: 978-3-540-44732-0

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