Inexact Multisubgraph Matching Using Graph Eigenspace and Clustering Models

  • Serhiy Kosinov
  • Terry Caelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

In this paper we show how inexact multisubgraph matching can be solved using methods based on the projections of vertices (and their connections) into the eigenspaces of graphs - and associated clustering methods. Our analysis points to deficiencies of recent eigenspectra methods though demonstrates just how powerful full eigenspace methods can be for providing filters for such computationally intense problems. Also presented are some applications of the proposed method to shape matching, information retrieval and natural language processing.

Keywords

Natural Language Processing Parse Tree Graph Match Shape Match Adjacency Matrice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    H. Bunke. Recent advances in structural pattern recognition with application to visual form analysis. IWVF4, LNCS, 2059:11–23, 2001.Google Scholar
  2. 2.
    L. Collatz and U. Sinogowitz. Spektren endlicher grafen. Abh. Math. Sem. Univ. Hamburg, 21:63–77, 1957.MATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    L. Dice. Measures of the amount of ecologic association between species. Ecology, 26:297–302, 1945.CrossRefGoogle Scholar
  4. 4.
    P. Dimitrov, C. Phillips, and K. Siddiqi. Robust and efficient skeletal graphs. Conference on Computer Vision and Pattern Recognition, june 2000.Google Scholar
  5. 5.
    X. Jiang, A. Munger, and H. Bunke. On median graphs: properties, algorithms, and applications. IEEE Trans. PAMI, 23(10):1144–1151, October 2001.Google Scholar
  6. 6.
    B. Luo and E. Hancock. Structural graph matching using the em algorithm and singular value decomposition. IEEE Trans. PAMI, 23(10):1120–1136, October 2001.Google Scholar
  7. 7.
    N. Maloy. Successor variety stemming: variations on a theme. 2000. project report (unpublished).Google Scholar
  8. 8.
    M. Pelillo, K. Siddiqi, and S. Zucker. Matching hierarchical structures using association graphs. IEEE Trans. PAMI, 21(11), November 1999.Google Scholar
  9. 9.
    A. Schwenk. Almost all trees are cospectral. Academic Press, New York-London, 1973.Google Scholar
  10. 10.
    L. Shapiro and J. Brady. Feature-based correspondence-an eigenvector approach. Image and Vision Computing, 10:268–281, 1992.CrossRefGoogle Scholar
  11. 11.
    A. Shokoufandeh and S. Dickinson. A unified framework for indexing matching hierarchical shape structures. IWVF4, LNCS, 2059:67–84, 2001.Google Scholar
  12. 12.
    K. Siddiqi, S. Bouix, A. Tannebaum, and S. Zucker. Hamilton-jacobi skeletons. To appear in International Journal of Computer Vision.Google Scholar
  13. 13.
    K. Siddiqi, A. Shokoufandeh, S. Dickinson, and S. Zucker. Shock graphs and shape matching. International Journal of Computer Vision, 30:1–24, 1999.Google Scholar
  14. 14.
    S. Tirthapura, D. Sharvit, P. Klein, and B. Kimia. Indexing based on edit-distance matching of shape graphs. Multimedia Storage and Archiving Systems III, 3527(2):25–36, 1998.Google Scholar
  15. 15.
    S. Umeyama. An eigen decomposition approach to weighted graph matching problems. IEEE Trans. PAMI, 10:695–703, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Serhiy Kosinov
    • 1
  • Terry Caelli
    • 1
  1. 1.Department of Computing Science Research Institute for Multimedia Systems (RIMS)The University of AlbertaEdmontonCanada

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