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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Aguilar, W.: Object recognition based on the structural correspondence of local features. MsThesis, UNAM, México (2006)Google Scholar
  2. 2.
    Bunke, H., Foggia, P., Guiobaldi, C., Vento, M.: Graph clustering using the weighted minimum common supergraph. In: Hancock, E.R., Vento, M. (eds.) GbRPR 2003. LNCS, vol. 2726, pp. 235–246. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Crandall, D., Felzenszwalb, P., Huttenlocher, D.: Spatial priors for part-based recognition using statistical models. In: Proc. Intl. Conf. on Computer Vision and Pattern Recognition, San Diego, CA, pp. 10–17 (2005)Google Scholar
  4. 4.
    Chung, F.R.K.: Spectral graph theory. In: Conference Board of Mathematical Science CBMS, American Matematical Society, Providence, RI, vol. 92 (1997)Google Scholar
  5. 5.
    Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594–611 (2006)CrossRefGoogle Scholar
  6. 6.
    Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. International Journal on Computer Vision 61(1), 57–59 (2005)CrossRefGoogle Scholar
  7. 7.
    Jain, B., Wysotzki, F.: Central clustering for attributed graphs. Machine Learning 56, 169–207 (2004)MATHCrossRefGoogle Scholar
  8. 8.
    Jiang, X., Münger, A., Bunke, H.: On median graphs: properties, algorithms, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1144–1151 (2001)CrossRefGoogle Scholar
  9. 9.
    Kadir, T., Brady, M.: Saliency, scale and image description. International Journal of Computer Vision 45(2), 83–105 (2001)MATHCrossRefGoogle Scholar
  10. 10.
    Kokkinos, I., Maragos, P., Yuille, A.L.: Bottom-up & top-down object detection using primal sketch features and graphical models. In: Proc. Intl. Conf. on Computer Vision and Pattern Recognition, New York, NY, pp. 1893–1900 (2006)Google Scholar
  11. 11.
    Kondor, R., Lafferty, J.: Diffusion kernels on graphs and other discrete input spaces. In: Proc. Intl. Conf. on Machine Learning, Los Altos CA, pp. 315–322 ( 2002)Google Scholar
  12. 12.
    Leung, T.K., Burl, M.C., Perona, P.: Finding faces in cluttered scenes using random labeled graph matching. In: Proc. IEEE Intl. Conf. on Computer Vision, Cambridge MA, pp. 637–644 ( 1995)Google Scholar
  13. 13.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Lozano, M.A., Escolano, F.: ACM attributed graph clustering for learning classes of images. In: Hancock, E.R., Vento, M. (eds.) GbRPR 2003. LNCS, vol. 2726, pp. 247–258. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Lozano, M.A., Escolano, F.: A significant improvement of softassign with diffusion kernels. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A., de Ridder, D. (eds.) Structural, Syntactic, and Statistical Pattern Recognition. LNCS, vol. 3138, pp. 76–84. Springer, Heidelberg (2004)Google Scholar
  16. 16.
    Lozano, M.A., Escolano, F.: Protein classification by matching and clustering surface graphs. Pattern Recognition 39(4), 539–551 (2006)MATHCrossRefGoogle Scholar
  17. 17.
    Suau, P., Escolano, F.: Bayesian optimization of the Kadir saliency filter. Submitted to Image and Vision Computing (2006)Google Scholar

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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