Constellations and the Unsupervised Learning of Graphs

  • Boyan Bonev
  • Francisco Escolano
  • Miguel A. Lozano
  • Pablo Suau
  • Miguel A. Cazorla
  • Wendy Aguilar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4538)


In this paper, we propose a novel method for the unsupervised clustering of graphs in the context of the constellation approach to object recognition. Such method is an EM central clustering algorithm which builds prototypical graphs on the basis of fast matching with graph transformations. Our experiments, both with random graphs and in realistic situations (visual localization), show that our prototypes improve the set median graphs and also the prototypes derived from our previous incremental method. We also discuss how the method scales with a growing number of images.


Unsupervised Learn Edge Density Graph Match Graph Cluster Visual Localization 
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 2007

Authors and Affiliations

  • Boyan Bonev
    • 1
  • Francisco Escolano
    • 1
  • Miguel A. Lozano
    • 1
  • Pablo Suau
    • 1
  • Miguel A. Cazorla
    • 1
  • Wendy Aguilar
    • 2
  1. 1.Robot Vision Group, Departamento de Ciencia de la Computación e IA, Universidad de AlicanteSpain
  2. 2.IIMAS: Instituto de Investigaciones en Matemáticas Aplicadas y Sistemas, Univesidad Nacional Autónoma de México UNAMMéxico

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