A New Technique For Texture Classification Using Markov Random Fields


  • Renato Salinas Universidad de Santiago de Chile Electrical Engineering Department Ave. Ecuador 3519, Santiago, Chile
  • Mauricio Gomez Universidad de Santiago de Chile Electrical Engineering Department Ave. Ecuador 3519, Santiago, Chile


Texture, backpropagation, configuration histograms, classification, Markov Random Fields


This paper proposes, applies and evaluates a new technique for texture classification in digital images. The work describes, as far as possible in a quantitative way, the concept of texture in digital images. Furthermore, we developed an innovative model that allows classifying and characterizing texture in digital images, to be used as a useful tool in noninvasive inspection of visual surfaces. The proposed methodology extracts the statistical order from an image of texture. The extraction of the high statistical order has been made using as a tool Markov Random Fields. The Backpropagation neural net is used for designing a classification module that will serve to test the performance of the configuration histograms, which are based on the statistical order. Furthermore, the research suggests the evaluation of the proposed technique from a qualitative perspective.


Haralick R., Shanmugam K. and Dinstein I., Textural Features for Image Classification, IEEE Trans. on Systems, Man, and Cybernetics, Vol. SMC-3, No. 6, pp. 610-621, 1973. http://dx.doi.org/10.1109/TSMC.1973.4309314

Hsin H., Texture Segmentation Using Modulated Wavelet Transform, IEEE Trans. on Image Processing, Vol 8. No. 7, 2000.

Hernández L., Torrealba V. and Reigosa A., Clasificación Automática del carcinoma de la mama, mediante un sistema de reconocimiento basado en redes neuronales, Memorias II Congreso Latinoamericano de Ingeniería Biomédica, La Habana, Cuba, 2001.

Mery D., Da Silva R., Calôva L. and Rebello J., Detección de fallas en piezas fundidas usando metodología de reconocimiento de patrones, 3rd Panamerican Conference for Nondestructive Testing, Rio de Janeiro, Brazil, 2003.

Bader D., JáJá J. and Chellapa R., Scalable Data Parallel Algorithms for Texture Synthesis using Gibbs Ramdon Fields, IEEE Transactions on Image Processing, vol. 4 No. 10, pp. 1456-1460, 1995. http://dx.doi.org/10.1109/83.465111

Pun C. and Lee M., Rotation Invariant Texture Classification Using a Two Stage Wavelet Packet Features Approach, IEE Proc. Vis. Image Signal Process, vol. No148, No6, pp 422-428, 2001.

Manjunath B., Simchony T. and Chellappa R., Stochastic and Deterministic Networks for Texture Segmentation, IEEE Transactions on Acoustics Speech and Signal Processing, vol. No6, pp. 1039- 1047, 1990.

Li S., Modeling Image Analysis Problems Using Markov Random Fields, Handbook of Statistics, vol. No 20, 2000.

Li S., Markov Random Fields Models in Computer Vision, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 866-869, 1994. http://dx.doi.org/10.1007/bfb0028368

Gómez M., Modelación y clasificación de texturas utilizando Campos Aleatorios de Markov, Master Thesis, Electrical Engineering, Universidad de Santiago de Chile, Santiago, Chile, 2004.

Brodatz P., Textures. New York: Dover, 1966.



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