Theoretical and Algorithmic Framework for Hypergraph Matching

  • Horst Bunke
  • Peter Dickinson
  • Miro Kraetzl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


Graphs have been successfully used in many disciplines of science and engineering. In the field of pattern recognition and image analysis, graph matching has proven to be a powerful tool. In this paper we generalize various matching tasks from graphs to the case of hypergraphs. We also discuss related algorithms for hypergraph matching.


  1. 1.
    Llados, J., Marti, E., Villanueva, J.: Symbol recognition by error-tolerant subgraph matching between region adjacency graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 23-10, 1137–1143 (2001)CrossRefGoogle Scholar
  2. 2.
    Luo, B., Hancock, E.: Structural graph matching using the EM algorithm and singular value decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence 23-10, 1120–1136 (2001)Google Scholar
  3. 3.
    Marcialis, G., Roli, F., Serrau, A.: Fusion of statistical and structural fingerprint classifiers. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 310–317. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. Journal of Pattern Recognition and Art. Intelligence 18(3), 265–298 (2004)CrossRefGoogle Scholar
  5. 5.
    Berge, C.: Hypergraphs. North-Holland, Amsterdam (1989)zbMATHGoogle Scholar
  6. 6.
    Bretto, A., Cherifi, H., Aboutajdine, D.: Hypergraph imaging: an overview. Pattern Recognition 35(3), 651–658 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Wong, A.C.K., Lu, S.W., Rioux, M.: Recognition and shape synthesis of 3-D objects based on attributed hypergraphs. IEEE Trans. PAMI 11(3), 279–290 (1989)Google Scholar
  8. 8.
    Demko, D.: Generalization of two hypergraphs. Algorithm of calculation of the greatest sub-hypergraph common to two hypergraphs annotated by semantic information. In: Jolion, J.-M., Kropatsch, W. (eds.) Graph Based Representations in Pattern Recognition, Computing, vol. (suppl. 12), pp. 1–10. Springer, Heidelberg (1998)Google Scholar
  9. 9.
    Bunke, H., Dickinson, P., Kraetzl, M.: Matching Hypergraphs, Technical Report, Intelligence Surveillance Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh SA 5111, Australia (2004)Google Scholar
  10. 10.
    Ullman, J.R.: An algorithm for subgraph isomorphism. Journal of ACM 23, 31–42 (1976)zbMATHCrossRefGoogle Scholar
  11. 11.
    McGregor, J.J.: Backtrack search algorithm and the maximal common subgraph problem. Software – Practice and Experience 12, 23–34 (1982)zbMATHCrossRefGoogle Scholar
  12. 12.
    Messmer, B.T., Bunke, H.: A new algorithm for error-tolerant subgraph isomorphism detection. IEEE Trans. PAMI 20(5), 493–507 (1998)Google Scholar
  13. 13.
    Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Recognition Letters 19, 255–259 (1998)zbMATHCrossRefGoogle Scholar
  14. 14.
    Jiang, X., Münger, A., Bunke, H.: On median graphs: properties, algorithms, and applications. IEEE Trans. PAMI 23(10), 1144–1151 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Horst Bunke
    • 1
  • Peter Dickinson
    • 2
  • Miro Kraetzl
    • 2
  1. 1.Institut für Informatik und angewandte MathematikUniversität BernBernSwitzerland
  2. 2.Intelligence Surveillance Reconnaissance DivisionDefence Science and, Technology OrganisationEdinburghAustralia

Personalised recommendations