Ant Colony Optimization for Image Regularization Based on a Nonstationary Markov Modeling

Ant colony optimization (ACO) has been proposed as a promising tool for regularization in image classification. The algorithm is applied here in a different way than the classical transposition of the graph color affectation problem. The 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 the same image segment. We show that this corresponds to an automatic adaptation of the neighborhood to the segment form, and that it outperforms the fixed-form neighborhood used in classical Markov random field regularization techniques. The performance of this new approach is illustrated on a simulated image and on actual remote sensing images

[1]  M. Bartlett.  The statistical analysis of spatial pattern , 1974, Advances in Applied Probability.

[2]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[3]  Mohamed Batouche,et al.  MRF-based image segmentation using Ant Colony System , 2003 .

[4]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[5]  Alain Hertz,et al.  Ants can colour graphs , 1997 .

[6]  William T. Freeman,et al.  Efficient Multiscale Sampling from Products of Gaussian Mixtures , 2003, NIPS.

[7]  Wojciech Pieczynski,et al.  Multisensor triplet Markov fields and theory of evidence , 2006, Image Vis. Comput..

[8]  Hossein Nezamabadi-pour,et al.  Edge detection using ant algorithms , 2006, Soft Comput..

[9]  Janet Bruten,et al.  Ant-like agents for load balancing in telecommunications networks , 1997, AGENTS '97.

[10]  Wojciech Pieczynski,et al.  Unsupervised image segmentation using triplet Markov fields , 2005, Comput. Vis. Image Underst..

[11]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[12]  Bruce Denby,et al.  Application of ant colony optimization to adaptive routing in aleo telecomunications satellite network , 2002, Ann. des Télécommunications.

[13]  Donald Geman,et al.  Boundary Detection by Constrained Optimization , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Xavier Descombes,et al.  Droplet Shapes for a Class of Models in $$\mathbb{Z}^2 $$ at Zero Temperature , 2003 .

[16]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[17]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[18]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[19]  Xavier Descombes,et al.  Fine Structures Preserving Markov Model for Image Processing , 1995 .

[20]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[21]  Xavier Descombes,et al.  Droplet shapes for a class of models in Z2 at zero temperature , 2003 .

[22]  Agostinho C. Rosa,et al.  Self-Regulated Artificial Ant Colonies on Digital Image Habitats , 2005, ArXiv.

[23]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[24]  Vittorio Maniezzo,et al.  The Ant System Applied to the Quadratic Assignment Problem , 1999, IEEE Trans. Knowl. Data Eng..

[25]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[26]  Xavier Descombes,et al.  Spatio-temporal fMRI analysis using Markov random fields , 1998, IEEE Transactions on Medical Imaging.

[27]  G. Rondeaux,et al.  Vegetation monitoring by remote sensing : a review of biophysical indices , 1995 .

[28]  M Dorigo,et al.  Ant colonies for the quadratic assignment problem , 1999, J. Oper. Res. Soc..

[29]  William T. Freeman,et al.  Nonparametric belief propagation , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[30]  E. Bonabeau,et al.  Routing in Telecommunications Networks with “ Smart ” Ant-Like Agents , 1998 .

[31]  Gianni A. Di Caro,et al.  AntNet: A Mobile Agents Approach to Adaptive Routing , 1999 .

[32]  Nicolas Monmarché,et al.  A new clustering algorithm based on the ants chemical recognition system , 2002, ECAI.

[33]  Donald Geman,et al.  Constrained Restoration and the Recovery of Discontinuities , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Giovanni Righini,et al.  Heuristics from Nature for Hard Combinatorial Optimization Problems , 1996 .

[35]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[36]  Martin Heusse,et al.  Adaptive Agent-Driven Routing and Load Balancing in Communication Networks , 1998, Adv. Complex Syst..