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Multispectral Random Field Models for Synthesis and Analysis of Color Images
March 1998 (vol. 20 no. 3)
pp. 327-332

Abstract—In this paper, multispectral extensions to the traditional gray level simultaneous autoregressive (SAR) and Markov random field (MRF) models are considered. Furthermore, a new image model is proposed, the pseudo-Markov model, which retains the characteristics of the multispectral Markov model, yet admits to a simplified parameter estimation method. These models are well-suited to analysis and modeling of color images. For each model considered, procedures are developed for parameter estimation and image synthesis. Experimental results, based on known image models and natural texture samples, substantiate the validity of these results.

[1] R.L. Kashyap and R. Chellappa,“Estimation and choice of neighbors in spatial-interaction models of images,” IEEE Trans. Information Theory, vol. 29, no. 1, pp. 60-72, Jan. 1983.
[2] A. Khotanzad and R.L. Kashyap, "Feature Selection for Texture Recognition Based on Image Synthesis," IEEE Trans. Systems, Man, and Cybernetics, vol. 17, no. 6, pp. 1,087-1,095, Nov. 1987.
[3] R. Chellappa, S. Chatterjee, and R. Bagdazian, "Texture Synthesis and Compression Using Gaussian-Markov Random Field Models," IEEE Trans. Systems, Man, and Cybernetics, vol. 15, no. 2, pp. 298-303, Mar. 1985.
[4] W.E. Larimore, "Statistical Inference on Stationary Random Fields," Proc. IEEE, vol. 65, pp. 961-970, June 1977.
[5] J.E. Besag, "Spatial Interaction and Statistical Analysis of Lattice Systems," J. Royal Statistical Soc., Series B, vol. 36, pp. 192-236, 1974.
[6] P. Whittle, "On Stationary Processes in the Plane," Biometrika, vol. 41, pp. 434-449, 1954.
[7] D.K. Panjwani and G. Healey, “Markov Random Field Models for Unsupervised Segmentation of Textured Color Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, pp. 939-954, 1995.
[8] A. Gagalowicz, S.D. Ma, and C. Tournier-Lasserve, "Efficient Models for Color Textures," Proc. Int'l Conf. Pattern Recognition, pp. 412-414,Paris, Oct. 1986.
[9] J. W. Bennett, Modeling and Analysis of Gray Tone, Color, and Multispectral Texture Images by Random Field Models and Their Generalizations, doctoral dissertation, Electrical Eng. Dept., Southern Methodist Univ., Dallas, 1997.
[10] Vision and Modeling Group, MIT Media Laboratory, "Vision Texture (VisTex) Database,", 1995.
[11] G.R. Cross and A.K. Jain, "Markov Random Field Texture Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 5, pp. 25-39, Jan. 1983.

Index Terms:
Color texture models, color texture synthesis, color texture analysis, multispectral random fields, multispectral simultaneous autoregressive models, multispectral Markov random field models, multispectral pseudo-Markov random field models, least squares estimation.
Jesse Bennett, Alireza Khotanzad, "Multispectral Random Field Models for Synthesis and Analysis of Color Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 327-332, March 1998, doi:10.1109/34.667889
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