Active Graph Matching Based on Pairwise Probabilities between Nodes

  • Xavier Cortés
  • Francesc Serratosa
  • Albert Solé-Ribalta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)


We propose a method to perform active graph matching in which the active learner queries one of the nodes of the first graph and the oracle feedback is the corresponding node of the other graph. The method uses any graph matching algorithm that iteratively updates a probability matrix between nodes (Graduated Assignment, Expectation Maximisation or Probabilistic Relaxation). The oracle’s feedback is used to update the costs between nodes and arcs of both graphs. We present and validate four different active strategies based on the probability matrix between nodes. It is not needed to modify the code of the graph-matching algorithms, since our method simply needs to read the probability matrix and to update the costs between nodes and arcs. Practical validation shows that with few oracle’s feedbacks, the algorithm finds the labelling that the user considers optimal because imposing few labellings the other ones are corrected automatically.


Machine Learning Active Graph Matching Interactive Graph Matching Least Confident Maximum Entropy Expected Model Change 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xavier Cortés
    • 1
  • Francesc Serratosa
    • 1
  • Albert Solé-Ribalta
    • 1
  1. 1.Departament d’Enginyeria Informàtica i MatemàtiquesUniversitat Rovira i VirgiliSpain

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