Skip to main content

A Novel Stochastic Attributed Relational Graph Matching Based on Relation Vector Space Analysis

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4179))

  • 825 Accesses

Abstract

In this paper, we propose a novel stochastic attributed relational graph (SARG) matching algorithm in order to cope with possible distortions due to noise and occlusion. The support flow and the correspondence measure between nodes are defined and estimated by analyzing the distribution of the attribute vectors in the relation vector space. And then the candidate subgraphs are extracted and ordered according to the correspondence measure. Missing nodes for each candidates are identified by the iterative voting scheme through an error analysis, and then the final subgraph matching is carried out effectively by excluding them. Experimental results on the synthetic ARGs demonstrate that the proposed SARG matching algorithm is quite robust and efficient even in the noisy environment. Comparative evaluation results also show that it gives superior performance compared to other conventional graph matching approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Wong, A.K.C., You, M.: Entropy and distance of random graphs with application to structural pattern recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 7(5) (September 1985)

    Google Scholar 

  2. Sanfeliu, A., Alquézar, R., Andrade, J., Climent, J., Serratosa, F., Vergés, J.: Graph-based representations and techniques for image processing and image analysis. Pattern Recognition 35, 639–650 (2002)

    Article  MATH  Google Scholar 

  3. Serratosa, F., Alquézar, R., Sanfeliu, A.: Function-described graphs for modelling objects represented by sets of attributes graphs. Pattern Recognition 36, 781–798 (2003)

    Article  Google Scholar 

  4. Li, S.Z.: Matching: invariant to translations, rotations and scale changes. Pattern Recognition 25, 583–594 (1992)

    Article  MathSciNet  Google Scholar 

  5. Christmas, W.J., Kittler, J., Petrou, M.: Structural matching in computer vision using probabilistic relaxation. IEEE Trans. Pattern Analysis and Machine Intelligence 17(8), 749–764 (1995)

    Article  Google Scholar 

  6. Messmer, B.T., Bunke, H.: A new algorithm for error-tolerant subgraph isomorphism detection. IEEE Trans. Pattern Analysis and Machine Intelligence 20(5), 493–503 (1998)

    Article  Google Scholar 

  7. Messmer, B.T., Bunke, H.: A decision tree approach to graph and subgraph isomorphism detection. Pattern Recognition 32, 1979–1998 (1999)

    Article  Google Scholar 

  8. Gold, S., Rangarajan, A.: A graduated assignment algorithm for graph matching. IEEE Trans. Pattern Analysis and Machine Intelligence 18(4), 377–388 (1996)

    Article  Google Scholar 

  9. Tsai, W.H., Fu, K.S.: Subgraph error-correcting isomorphisms for syntactic pattern recognition. IEEE Trans. Systems Man and Cybernetics 13(1), 48–62 (1983)

    MATH  MathSciNet  Google Scholar 

  10. El-Sonbaty, Y., Ismail, M.A.: A new algorithm for subgraph optimal isomorphism. Pattern Recognition 31(2), 205–218 (1998)

    Article  Google Scholar 

  11. Herault, L., Horaud, R., Veillon, F., Niez, J.J.: Symbolic image matching by simulated annealing. In: Proc. British Machine Vision Conference, Oxford, pp. 319–324 (1990)

    Google Scholar 

  12. Krcmar, M., Dhawan, A.: Application of genetic algorithms in graph matching. In: Proc. Int’l. Conf. Neural Networks, vol. 6, pp. 3872–3876 (1994)

    Google Scholar 

  13. van Wyk, B.J., van Wyk, M.A.: The spherical approximation graph matching algorithm. In: Proc. Int’l. Workshop on Multidiscilinary Design Optimization, pp. 280–288 (August 2000)

    Google Scholar 

  14. van Wyk, B.J., Clark, J.: An algorithm for approximate least-squares attributed graph matching. In: Problems in Applied Mathematics and Computational Intelligence, pp. 67–72 (2000)

    Google Scholar 

  15. van Wyk, M.A., Durrani, T.S., van Wyk, B.J.: A RKHS interpolator-based graph matching algorithm. IEEE Trans. Pattern Analysis and Machine Intelligence 24(7), 988–995 (2002)

    Article  Google Scholar 

  16. van Wyk, B.J., van Wyk, M.A.: Kronecker product graph matching. Pattern Recognition 36, 2019–2030 (2003)

    Article  MATH  Google Scholar 

  17. Nevatia, R., Babu, K.R.: Line extraction and description. Computer Graphics and Image Processing 13(1), 250–269 (1980)

    Google Scholar 

  18. Park, B.G., Lee, K.M., Lee, S.U., Lee, J.H.: Recognition of partially occluded objects using probabilistic ARG-based matching. Computer Vision and Image Understanding 90(3), 217–241 (2003)

    Article  MATH  Google Scholar 

  19. Kim, D.H., Yun, I.D., Lee, S.U.: A new attributed relational graph matching algorithm using the nested structure of earth mover’s distance. In: Proceedings of IEEE International conference on Pattern Recognition, Cambridge, UK, pp. 48–51 (August 2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Park, B.G., Lee, K.M., Lee, S.U. (2006). A Novel Stochastic Attributed Relational Graph Matching Based on Relation Vector Space Analysis. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_89

Download citation

  • DOI: https://doi.org/10.1007/11864349_89

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44630-9

  • Online ISBN: 978-3-540-44632-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics