Use of Ant Colony Optimization for Finding Neighborhoods in Image Non-stationary Markov Random Field Classification

  • Sylvie Le Hégarat-Mascle
  • Abdelaziz Kallel
  • Xavier Descombes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)


In global classifications using Markov Random Field (MRF) modeling, the neighborhood form is generally considered as independent of its location in the image. Such an approach may lead to classification errors for pixels located at the segment borders. The solution proposed here consists in relaxing the assumption of fixed-form neighborhood. Here we propose to use the Ant Colony Optimization (ACO) and to exploit its ability of self-organization. Modeling upon the behavior of social insects for computing strategies, the ACO ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favoring paths within a same image segment. We show that this corresponds to an automatic adaptation of the neighborhood to the segment form. Performance of this new approach is illustrated on a simulated image and on actual remote sensing images SPOT4/HRV.


Markov Random Fields Label Image Find Neighborhood Markov Random Fields Modeling Pixel Label 
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.


  1. 1.
    Geman, S., Geman, D.: Stochastic relaxation, gibbs distribution and bayesian restoration of images. IEEE Trans. on PAMI 6, 721–741 (1984)zbMATHGoogle Scholar
  2. 2.
    Geman, D., Geman, S., Graffigne, C., Dong, P.: Boundary detection by constrained optimization. IEEE Trans. on PAMI 12, 609–628 (1990)Google Scholar
  3. 3.
    Geman, S., Reynolds, G.: Constrained restoration and recovery of discontinuities. IEEE Trans. on PAMI 14, 367–383 (1992)Google Scholar
  4. 4.
    Descombes, X., Kruggel, F., von Cramon, Y.: Spatio-temporal fmri analysis using markov random fields. IEEE Trans. on Medical Imaging 17(6), 1028–1039 (1998)CrossRefGoogle Scholar
  5. 5.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. on Systems, Man, and Cyber. 26, 29–41 (1996)CrossRefGoogle Scholar
  6. 6.
    Yao, X.: Evolutionary Computation: Theory and Applications. World Scientific, Singapore (1999)Google Scholar
  7. 7.
    Tan, K., Lim, M., Yao, X., L., P., W. (eds.): Recent Advances in simulated Evolution And Learning. World Scientific, Singapore (2004)zbMATHGoogle Scholar
  8. 8.
    Rodríguez-Vázquez, K., Fonseca, C.M., Fleming, P.J.: Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming. IEEE Trans. on Sys., Man, and Cyber. 34(4), 531–545 (2004)CrossRefGoogle Scholar
  9. 9.
    Yeun, Y.S., Ruy, W.S., Yang, Y.S., Kim, N.J.: Implementing linear models in genetic programming. IEEE Trans. Evol. Comp. 8(6), 542–566 (2004)CrossRefGoogle Scholar
  10. 10.
    di Caro, G., Dorigo, M.: Antnet: distributed stigmeric control for communications networks. J. of Artificial Intelligence Research 9, 317–365 (1998)zbMATHGoogle Scholar
  11. 11.
    Sigel, E., Denby, B., Le Hégarat-Mascle, S.: Application of ant colony optimization to adaptive routing in a leo telecommunications satellite network. Ann. of Telecommunications 57, 520–539 (2002)Google Scholar
  12. 12.
    Colorni, A., Dorigo, M., Maffioli, F., Maniezzo, V., Righini, G., Trubian, M.: Heuristics from nature for hard combinatorial problems. Int. Trans. in Operat. Research 3, 1–21 (1996)zbMATHCrossRefGoogle Scholar
  13. 13.
    Costa, D., Hertz, A.: Ants can colour graphs. J. of the Operat. Resea. Soc. 48, 295–305 (1997)zbMATHGoogle Scholar
  14. 14.
    Dorigo, M., Gambardella, L.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evol. Comp. 1, 53–66 (1997)CrossRefGoogle Scholar
  15. 15.
    Le Hégarat-Mascle, S., Kallel, A., Descombes, X.: Ant colony optimization for image regularization based on a non-stationary markov modeling. IEEE Trans. on Image Processing 16(3), 865–878 (2007)CrossRefGoogle Scholar
  16. 16.
    Besag, J.: On the statistical analysis of dirty pictures. J. of the Royal Statistical Society, Series B 3(48), 259–302 (1986)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sylvie Le Hégarat-Mascle
    • 1
  • Abdelaziz Kallel
    • 2
  • Xavier Descombes
    • 3
  1. 1.IEF/AXIS, Université de Paris-Sud 91405, Orsay CedexFrance
  2. 2.CETP/IPSL, 10, 12 avenue de l’Europe 78140, VélizyFrance
  3. 3.CNRS/INRIA/UNSA, INRIA, 06902 Sophia Antipolis, CedexFrance

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