Community Detection for Hierarchical Image Segmentation

  • Arnaud Browet
  • P. -A. Absil
  • Paul Van Dooren
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6636)


In this paper, we present a new graph-based technique to detect segments or contours of objects in a given picture. Our algorithm is designed as an approximation of the Louvain method that unfolds the community structures in a large graph. Without any a priori knowledge on the input picture, relevant regions are extracted while the optimal definition of a contour, depending on the user or the application, can be tuned using parameters. The communities found are also hierarchical allowing to find subregions inside an object. We present experimental results of our method on real images.


Image segmentation community detection modularity optimization 


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  1. 1.
    Alzate, C., Suykens, J.A.K.: Sparse kernel models for spectral clustering using the incomplete cholesky decomposition. In: IJCNN, pp. 3556–3563 (2008)Google Scholar
  2. 2.
    Alzoubi, H., Pan, W.D.: Fast and accurate global motion estimation algorithm using pixel subsampling. Information Sciences 178(17), 3415 (2008)CrossRefGoogle Scholar
  3. 3.
    Beucher, S.: The watershed transformation applied to image segmentation (1991)Google Scholar
  4. 4.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008(10), 10008 (2008)CrossRefGoogle Scholar
  5. 5.
    Cox, I., Rao, S., Zhong, Y.: ”ratio regions”: A technique for image segmentation. In: International Conference on Pattern Recognition, vol. 2, p. 557 (1996)Google Scholar
  6. 6.
    Delvenne, J.C., Yaliraki, S.N., Barahona, M.: Stability of graph communities across time scales. PNAS 107(29), 12755–12760 (2010)CrossRefGoogle Scholar
  7. 7.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59, 167–181 (2004)CrossRefGoogle Scholar
  8. 8.
    Fortunato, S.: Community detection in graphs. Physics Reports (2010)Google Scholar
  9. 9.
    Frederix, K., Barel, M.V.: Sparse spectral clustering method based on the incomplete cholesky decomposition. Report TW552, Katholieke Universiteit Leuven (2009)Google Scholar
  10. 10.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 99(12), 7821–7826 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Good, B.H., de Montjoye, Y.A., Clauset, A.: Performance of modularity maximization in practical contexts. Physical Review E 81(4), 046106+ (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ho, J., Lee, K.C., Yang, M.H., Kriegman, D.: Visual tracking using learned linear subspaces. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I–782–I–789 (2004)Google Scholar
  13. 13.
    Olszewska, J.I.: Unified framework for multi-feature active contour. Ph.D. thesis, Universite catholique de Louvain, Ecole polytechnique (2009)Google Scholar
  14. 14.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988), doi:10.1007/BF00133570CrossRefzbMATHGoogle Scholar
  15. 15.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar
  16. 16.
    Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Physical review. E, Statistical, nonlinear, and soft matter physics 69(2 Pt 2) (2004)Google Scholar
  17. 17.
    Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E. Stat. Nonlin. Soft Matter Phys. 74(1 Pt 2) (2006)Google Scholar
  18. 18.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  19. 19.
    Sundaramoorthi, G., Mennucci, A., Soatto, S., Yezzi, A.: Tracking deforming objects by filtering and prediction in the space of curves. In: CDC (December 2009)Google Scholar
  20. 20.
    Wang, S., Siskind, J.M.: Image segmentation with minimum mean cut. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 517–524 (2001)Google Scholar
  21. 21.
    Werman, M., Peleg, S., Rosenfeld, A.: A distance metric for multidimensional histograms. Computer Vision, Graphics, and Image Processing 32(3), 328–336 (1985)CrossRefzbMATHGoogle Scholar
  22. 22.
    Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1101–1113 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Arnaud Browet
    • 1
  • P. -A. Absil
    • 1
  • Paul Van Dooren
    • 1
  1. 1.ICTEAM InstituteUniversité catholique de LouvainLouvain-la-NeuveBelgium

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