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)


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.


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.


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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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