Featured based Segmentation of Color Textured Images using GLCM and Markov Random Field Model

In this paper, we propose a new image segmentation approach for colour textured images. The proposed method for image segmentation consists of two stages. In the first stage, textural features using gray level co-occurrence matrix(GLCM) are computed for regions of interest (ROI) considered for each class. ROI acts as ground truth for the classes. Ohta model (I1, I2, I3) is the colour model used for segmentation. Statistical mean feature at certain inter pixel distance (IPD) of I2 component was considered to be the optimized textural feature for further segmentation. In the second stage, the feature matrix obtained is assumed to be the degraded version of the image labels and modeled as Markov Random Field (MRF) model to model the unknown image labels. The labels are estimated through maximum a posteriori (MAP) estimation criterion using ICM algorithm. The performance of the proposed approach is compared with that of the existing schemes, JSEG and another scheme which uses GLCM and MRF in RGB colour space. The proposed method is found to be outperforming the existing ones in terms of segmentation accuracy with acceptable rate of convergence. The results are validated with synthetic and real textured images.

• Dipti Patra
• Mridula J

• Texture Image Segmentation
• Gray Level Cooccurrence Matrix
• Markov Random Field Model
• Ohta colour space
• ICM algorithm.

[1] John C. Russ, "The image processing Handbook," 5th edition, CRC Press, Boca Raton, FloridA
[2] B. Kartikeyan, A. Sarkar, and K. Majumdar, "A segmentation approach to classification of remote sensing imagery," International Journal of Remote Sensing, Vol. 19, No.9, pp. 1695-1709, 1998.
[3] R. M. Haralick, K. Shanmugam and T. Dinstein, T, " Textural features for image classification," IEEE Trans. Syst., Man, Cybern., SMC-3, pp. 610-621, 1973.
[4] R. M. Haralick, "Statistical and structural approaches to texture," Proceedings of IEEE, Vol 67, No. 5, 1979.
[5] L. Wang and D. C. He, "A new statistical approach for textural analysis," Photogrammetric Engineering and Remote Sensing, Vol. 56, pp. 61-66, 1990.
[6] R.W.Connors, M.H.Trivedi, and C. A. Harlow, "Segmentation of a high resolution urban scene using texture operators," Comput. Graphics Image Processing, vol. 25, pp. 273-310, 1984.
[7] P.V. Narasimha Rao, M. V. R. Sesha Sai, K. Sreenivas, M. V. Krishna Rao, B. R. M. Rao, R. S. Dwivedi and L. Venkataratnam, "Textural analyses of IRS-1D panchromatic data for land cover classification," International Journal of Remote Sensing, Vol. 23, No. 17, pp. 3327-3345, 2002.
[8] Anne Puissant, Jacky Hirsch and Christian Weber, "The utility of textural analysis to improve per-pixel classification for high to very high spatial resolution imagery," International Journal of remote Sensing, Vol. 26, No. 4, pp. 733-745, 2005.
[9] Z. Kato, T. C. Pong, "A Markov random field image segmentation model for colored textured images," Image. Vision. Comp. Vol. 24, pp. 1103-1114, 2006.
[10] Z. Kato, T. C. Pong and S. G. Qiang, "Multicue MRF image segmentation: combining texture and color features,". IEEE Comp. society. ICPR, 2002.
[11] J. Besag: "On the statistical analysis of dirty pictures," fRoy. Statist.Soc.B. Vol. 62, pp.259-302, 1986.
[12] Brandt C. K. Tso and Paul M. Mather , "Classification methods for remotely sensed data," 2nd Edition, CRC press, 2009.
[13] F. Destrempes, M. Mignotte and J. F. Angers, "A stochastic method for Bayesian estimation of hidden Markov random field models with
application to a color model," IEEE. Trans. Image. Processing, Vol. 14, pp. 1096-1108, 2005.
[14] Rahul Dey, P. K. Nanda and Sucheta Panda, "Constrained Markov Random Field Model for Color and Texture Image Segmentation,". In Proceedings of the IEEE International Conference on Signal processing, Communications and Networking (Chennai, India, Jan 04 — 06, 2008.