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Distance Measures between Attributed Graphs and Second-Order Random Graphs

  • Francesc Serratosa
  • Alberto Sanfeliu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

The aim of this article is to purpose a distance measure between Attributed Graphs (AGs) and Second-Order Random Graphs (SORGs) for recognition and classification proposes. The basic feature of SORGs is that they include both marginal probability functions and joint probability functions of graph elements (vertices or arcs). This allows a more precise description of both the structural and semantic information contents in a set (or cluster) of AGs and, consequently, an expected improvement in graph matching and object recognition. The distance measure is derived from the probability of instantiating a SORG into an AG.

SORGs are shown to improve the performance of other random graph models such as FORGs and FDGs and also the direct AG-to-AG matching in two experimental recognition tasks.

Keywords

Joint Probability Random Graph Random Element Attribute Graph Graph Element 
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.
    Alquézar, R., Serratosa, F., Sanfeliu, A.: Distance between Attributed Graphs and Function- Described Graphs relaxing 2nd order restrictions. In: Amin, A., Pudil, P., Ferri, F., Iñesta, J.M. (eds.) SPR 2000 and SSPR 2000. LNCS, vol. 1876, pp. 277–286. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Peleg, S., Rosenfeld, A.: Determining compatibility coefficients for curve enhancement relaxation processes. IEEE Transactions on Systems, Man and Cybernetics 8, 548–555 (1978)CrossRefGoogle Scholar
  3. 3.
    Sengupta, K., Boyer, K.: Organizing large structural model bases. IEEE Trans. on Pattern Analysis and Machine Intelligence 17, 321–332 (1995)CrossRefGoogle Scholar
  4. 4.
    Wong, A.K.C., You, M.: Entropy and distance of random graphs with application to structural pattern recognition. IEEE Trans. on PAMI. 7, 599–609 (1985)zbMATHGoogle Scholar
  5. 5.
    Serratosa, F., Alquézar y, R., Sanfeliu, A.: Estimating the Joint Probability Distribution of Random Vertices and Arcs by means of Second-order Random Graphs. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 252–262. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Serratosa, F., Alquézar y, R., Sanfeliu, A.: Synthesis of function-described graphs and clustering of attributed graphs. International Journal of Pattern Recognition and Artificial Intelligence 16(6), 621–655 (2002)CrossRefGoogle Scholar
  7. 7.
    Serratosa, F., Alquézar y, R., Sanfeliu, A.: Function-described for modeling objects represented by attributed graphs. Pattern Recognition 36(3), 781–798 (2003)CrossRefGoogle Scholar
  8. 8.
    Sanfeliu, A., Fu, K.: A distance measure between attributed relational graphs for pattern recognition. IEEE Transactions on Systems, Man and Cybernetics 13, 353–362 (1983)zbMATHGoogle Scholar
  9. 9.
    Felzenswalb, P.F., Huttenlocher, D.P.: Image Segmentation Using Local Variation. In: Proc. of the IEEE Computer Soc. Conf. on Computer Vision and Pattern Recognition, pp. 98–104 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Francesc Serratosa
    • 1
  • Alberto Sanfeliu
    • 2
  1. 1.Dept. d’Enginyeria Informàtica i MatemàtiquesUniversitat Rovira I VirgiliSpain
  2. 2.Institut de Robòtica i Informàtica IndustrialUniversitat Politècnica de CatalunyaSpain

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