Relevance Criteria for Data Mining Using Error-Tolerant Graph Matching

  • Sidharta Gautama
  • Rik Bellens
  • Guy De Tré
  • Johan D’Haeyer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4040)


In this paper we present a graph based approach for mining geospatial data. The system uses error-tolerant graph matching to find correspondences between the detected image information and the geospatial vector data. Spatial relations between objects are used to find a reliable object-to-object mapping. Graph matching is used as a flexible query mechanism to answer the spatial query. A condition based on the expected graph error has been presented which allows to determine the bounds of error tolerance and in this way characterizes the relevancy of a query solution. We show that the number of null labels is an important measure to determine relevancy. To be able to correctly interpret the matching results in terms of relevancy the derived bounds of error tolerance are essential.


Point Pattern Graph Match Spatial Query Maximum Clique Problem Graph Base Approach 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mizzaro, S.: Relevance: The whole history. Journal of the American Society for Information Science 48(9), 810–832 (1997)CrossRefGoogle Scholar
  2. 2.
    Lavrenko, V.: Optimal Mixture Models in IR. In: Crestani, F., Girolami, M., van Rijsbergen, C.J.K. (eds.) ECIR 2002. LNCS, vol. 2291, pp. 193–212. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Rosenfeld, R.: Two decades of Statistical Language Modeling: Where Do We Go From Here? Proceedings of the IEEE 88(8), 1270–1278 (2000)CrossRefGoogle Scholar
  4. 4.
    Zhou, X., Huang, T.: Relevance feedback in content-based image retrieval: some recent advances Source. Information Sciences–Applications 148(1-4), 129–137 (2002)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Tieu, K., Viola, P.: Boosting image retrieval. International Journal of Computer Vision 56(1), 17–36 (2004)CrossRefGoogle Scholar
  6. 6.
    Besl, P., McKay, N.: A method for registration of 3-D shapes. IEEE Trans. Pat. Anal. and Mach. Intel. 14(2), 239–256 (1992)CrossRefGoogle Scholar
  7. 7.
    Bomze, I.M., Budinich, M., Pardalos, P.M., Pelillo, M.: The maximum clique problem. In: Du, D.Z., Pardalos, P.M. (eds.) Handbook of Combinatorial Optimization, vol. 4. Kluwer Academic Publishers, Boston (1999)Google Scholar
  8. 8.
    Christmas, W., Kittler, J., Petrou, M.: Structural Matching in Computer Vision Using Probabilistic Relaxation. IEEE Trans. Pat. Anal. and Mach. Intel. 17(8), 749–764 (1995)CrossRefGoogle Scholar
  9. 9.
    Gautama, S., D’Haeyer, J., Philips, W.: Image-based change detection of geographic information using spatial constraints. In: De Caluwe, R., De Tre, G., Bordogna, G. (eds.) Flexible Querying and Reasoning in Spatio-Temporal Databases: Theories and Applications, pp. 351–368. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Hummel, R., Zucker, S.: On the foundations of relaxation labeling processes. IEEE Trans. Pat. Anal. and Mach. Intel. 5(3), 742–776 (1983)Google Scholar
  11. 11.
    Li, S.: Markov Random Field Modeling in Computer Vision. Springer, New-York (1995)Google Scholar
  12. 12.
    Pelillo, M., Jagota, A.: Feasible and infeasible maxima in a quadratic program for maximum clique. Journal of Artif. Neural Networks 2(4), 411–420 (1995)Google Scholar
  13. 13.
    Wilson, R.: Inexact Graph Matching Using Symbolic Constraints. Ph. D. thesis, Department of Computer Science, University of York (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sidharta Gautama
    • 1
  • Rik Bellens
    • 1
  • Guy De Tré
    • 1
  • Johan D’Haeyer
    • 1
  1. 1.TELINGhent UniversityGentBelgium

Personalised recommendations