Abstract
The use of Gibbs random fields (GRF) to model images poses the important problem of the dependence of the patterns sampled from the Gibbs distribution on its parameters. Sudden changes in these patterns as the parameters are varied are known asphase transitions. In this paper we concentrate on developing a general deterministic theory for the study of phase transitions when a single parameter, namely, the temperature, is varied. This deterministic framework is based on a technique known as themean-field approximation, which is widely used in statistical physics. Our mean-field theory is general in that it is valid for any number of gray levels, any pairwise interaction potential, any neighborhood structure or size, and any set of constraints imposed on the desired images. The mean-field approximation is used to compute closed-form estimates of the critical temperatures at which phase transitions occur for two texture models widely used in the image modeling literature: the Potts model and the autobinomial model. The mean-field model allows us to gain insight into the Gibbs model behavior in the neighborhood of these temperatures. These analytical results are verified by computer simulations that use a novel mean-field descent algorithm. An important spinoff of our mean-field theory is that it allows us to compute approximations for the correlation functions of GRF models, thus bridging the gap between neighborhood-based and correlation-baseda priori image models.
Similar content being viewed by others
References
R. Szeliski,Bayesian Modeling of Uncertainty in Low-Level Vision, Kluwer Academic, Dordrecht, The Netherlands, 1989.
J.J. Clark and A.L. Yuille,Data Fusion for Sensory Information Processing Systems, Kluwer Academic, Dordrecht, The Netherlands, 1990.
T. Poggio and V. Torre, “Ill posed problems and regularization analysis in early vision,” Memo 773, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 1984.
G.R. Cross and A.K. Jain, Markov random field texture models,IEEE Trans. Patt. Anal. Mach. Intell., vol. 5, 1983, pp. 25–39.
P. Davies, ed.,The New Physics, Cambridge University Press, London, 1989.
I.M. Elfadel and R.W. Picard, “Miscibility matrices explain the behavior of grayscale textures generated by Gibbs random fields,”Proc. Soc. Photo-Opt. Instrum. Eng., vol. 1381, 1990, pp. 524–535.
R.W. Picard and I.M. Elfadel, “On the structure of aura and co-occurrence matrices for the Gibbs texture model,”J. Math. Imag. Vis., vol. 2, 1992, pp. 5–25.
R.W. Picard, “Texture modeling: Temperature effects on Markov/Gibbs random fields,” Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA, 1991.
R.W. Picard and A.P. Pentland, “Markov/Gibbs image modeling: Temperature and texture,”Proc. Soc. Photo-Opt. Instrum. Eng., vol. 1607, 1991, pp. 15–26.
R.C. Dubes and A.K. Jain, “Random field models in image analysis,”J. Appl. Statist., vol. 16, 1989, pp. 131–163.
D. Geman, “Random fields and inverse problems in imaging,” Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA, 1989, preprint.
G. Parisi,Statistical Field Theory, Addison-Wesley, Reading, MA, 1988.
A.L. Yuille, “Generalized deformable models, statistical physics, and matching problems,”Neural Comput., vol. 2, 1990, pp. 1–24.
S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,”IEEE Trans. Patt. Anal. Mach. Intell., vol. 6, 1984, pp. 721–741.
D.J. Amit,Modeling Brain Function, Cambridge University Press, London, 1989.
D. Geiger and F. Girosi, “Parallel and deterministic algorithms from MRF's: Surface reconstruction,”IEEE Trans. Patt. Anal. Mach. Intell., vol. 13, 1991, pp. 401–412.
D. Geiger and A. Yuille, “A common framework for image segmentation,”Internat. J. Comput. Vis., vol. 6, 1991, pp. 227–253.
A. Lumsdaine, J. Wyatt, and I. Elfadel, “Nonlinear analog networks for image smoothing and segmentation,J. VLSI Signal Process., vol. 3, 1991, pp. 53–68.
A. Blake, “The least disturbance principle and weak constraints,”Patt. Recog. Lett., vol. 1, 1983, pp. 393–399.
I.M. Elfadel and A.L. Yuille, “Mean-field theory for grayscale texture synthesis using Gibbs random fields,”Proc. Soc. Photo-Opt. Instrum. Eng., vol. 1569, 1991, pp. 248–259.
C. Peterson and B. Söderberg, “A new method for mapping optimization problems onto neural networks,”Internat. J. Neural Systems, vol. 1, 1989, pp. 3–22.
P.D. Simic, “Statistical mechanics as the underlying theory of “elastic” and “neural” optimization,”Network, vol. 1, 1990, pp. 89–103.
A. Papoulis,Probability, Random Variables, and Stochastic Processes, McGraw-Hill, New York, 1984.
I.M. Elfadel, “From random fields to networks,” Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA, 1992.
G.L. Bilbro, W.E. Snyder, S.J. Garnier, and J.W. Gault, “Mean field annealing: A formalism for constructing GNC-like algorithms,”IEEE Trans. Neural Networks, vol. 3, 1992, pp. 131–138.
A. Blake and A. Zisserman,Visual Reconstruction, MIT Press, Cambridge, MA, 1987.
C. Peterson, “Parallel distributed approaches to combinatorial optimization problems — benchmark studies on tsp,”Neural Comput., vol. 2, 1990, pp. 261–270.
D.G. Luenberger,Linear and Nonlinear Programming, 2nd ed., Addison-Wesley, Readings, MA, 1984.
S.K. Ma,Modern Theory of Critical Phenomena, Benjamin, New York, 1976.
R. Durbin, R. Szeliski, and A. Yuille, “An analysis of the elastic net approach to the travelling salesman problem,”Neural Comput., vol. 1, 1989, pp. 348–358.
A.K. Jain,Fundamentals of Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1989.
P.J. Davis,Circulant Matrices, Wiley, New York, 1979.
R.L. Kashyap, “Univariate and multivariate random field models for images,”Comput. Graph. Image Process., vol. 12, 1980, pp. 257–270.
H. Derin and H. Elliott, “Modeling and segmentation of noisy and textured images using Gibbs random fields,”IEEE Trans. Patt. Anal. Mach. Intell. vol. 9, 1987, pp. 39–55.
R.L. Kashyap and P.M. Lapsa, “Synthesis and estimation of random fields using long-correlation models,”IEEE Trans. Patt. Anal. Mach. Intell., vol. 6, 1984, pp. 800–809.
Author information
Authors and Affiliations
Additional information
The work of I.M. Elfadel was supported in part by the National Science Foundation under grant MIP-91-17724. The work of A.L. Yuille was supported by the Brown, Harvard, and MIT Center for Intelligent Control Systems under U.S. Army Research Office grant DAAL03-86-C-0171, by the Defense Advanced Research Projects Agency under contract AFOSR-89-0506, and by the National Science Foundation under grant IRI-9003306.
Rights and permissions
About this article
Cite this article
Elfadel, I.M., Yuille, A.L. Mean-field phase transitions and correlation functions for Gibbs random fields. J Math Imaging Vis 3, 167–186 (1993). https://doi.org/10.1007/BF01250528
Issue Date:
DOI: https://doi.org/10.1007/BF01250528