We have considered a texture to be a stochastic, possible periodic, two-dimensional image field. We have used Markov Random Fields as texture models. We considered binomial model, where each point in the texture has a binomial distribution with parameter controlled by its neighbors’ and the number of gray levels. The parameters of the Markov random field control the strength and direction of the clustering in the image. The power of the binomial model to produce blurry, sharp, line-like, and blob-like textures is demonstrated. Generated textures are then estimated using one of the approximated Maximum likelihood estimation called as Coding method.

1. Stan Z. Li, “Markov random field modeling in image analysis”, Advances in Pattern Recognition, Springer-Verlag London Limited 2009
2. R. Chellappa and S. Chatterjee, “Classification of textures using Gaussian Markov random fields”, IEEE Transactions on Acoustics, Speech, and Signal Processing. vol ASSP- 33,No-4, pp.959-963, 1985.
3. G. C. Cross and A. K. Jain, “Markov random Field texture models” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-5 No 1 , pp. 25-39, 1983.
4. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-6 No 6 pp. 721-741, 1984.
5. J. Besag, “Spatial interaction and the statistical analysis of lattice systems (with discussion),” J. Royal Statist. Soc., series B, vol. 36, pp. 192-326, 1974. 6] M. Hassner and J. Sklansky, "The use of Markov random fields as models of texture," Comput. Graphics Image Processing, vol. 12, pp. 35 7-370, 1980.
6. R. W. Connors and C. A. Harlow, "A theoretical comparison of texture algorithms," IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-2, pp. 204-222, 1980.

Binomial model Maximum likelihood estimation Coding method Markov random field texture stochastic