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Classification of color textures with random field models and neural networks
Keywords:
Color Texture, Color Texture Features, Mutispectral Random Field Models, Texture ClassificationAbstract
A number of texture classification approaches have been developed in the past but most of these studies target graylevel textures. In this work, novel results are presented on Neural Network based classification of color textures in a very large heterogeneous database. Several different Multispectral Random Field models are used to characterize the textures. The classifying features are based on the estimated parameters of these model and functions defined on them. The approach is tested on a database of 73 different color textures classes. The advantage of utilizing color information is demonstrated by converting color textures to gray-level ones and classifying them using Grey Level Co-Occurrence Matrix (GLCM) based features.
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References
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[2] A. Drimbarean and P. F. Whelan, Experiments in Color Texture Analysis, Pattern Recognition Letters, vol. 22, Aug. 2001, pp. 1161-1167.
[3] G. Paschos, Fast Color Recognition Using Chromaticity Moments, Pattern Recognition Letters, vol. 21, Aug. 2000, pp. 837-841.
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[5] B. Thai and G. Healey, Optimal Spatial Filter Selection for Illumination-Invariant Color Texture Discrimination, IEEE Trans. on Systems, Man, and Cybernetics – Part B: Cybernetics, vol. 30, no. 4, Aug. 2000, pp. 610-616.
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[7] PH Suen and G. Healey, Modeling and Classifying Color Textures Using Random Fields in a Random Environment, Pattern Recognition, vol. 32, June 1999, pp. 1009-1017.
[8] K. B. Eom, Segmentation of Monochrome and Color Textures Using Moving Average Modeling Approach, Image Vision Computing, vol. 3, no. 17, 1999, pp. 233–244.
[9] E. Oja and K. Valkealahti, Compressing HigherOrder Co-Occurrence for Texture Analysis Using the SelfOrganizing Map, Proceedings of the IEEE International Conference on Neural Networks, vol. 2, Nov.-Dec. 1995, pp. 1160-1164.
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[14] S. Haykin, Neural Networks: Multilayer Perceptrons, (1994) 138-229, Macmillan College Publishing Company, NJ.
[2] A. Drimbarean and P. F. Whelan, Experiments in Color Texture Analysis, Pattern Recognition Letters, vol. 22, Aug. 2001, pp. 1161-1167.
[3] G. Paschos, Fast Color Recognition Using Chromaticity Moments, Pattern Recognition Letters, vol. 21, Aug. 2000, pp. 837-841.
[4] A. Jain and G. Healey, A Multiscale Representation Including Opponent Color Features for Texture Recognition, IEEE Trans. on Image Processing, vol. 7, no. 1, Jan. 1998, pp. 124-128.
[5] B. Thai and G. Healey, Optimal Spatial Filter Selection for Illumination-Invariant Color Texture Discrimination, IEEE Trans. on Systems, Man, and Cybernetics – Part B: Cybernetics, vol. 30, no. 4, Aug. 2000, pp. 610-616.
[6] M. Mirmehdi and M. Petrou, Segmentation of Color Textures, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 2, Feb. 2000, pp. 142-159.
[7] PH Suen and G. Healey, Modeling and Classifying Color Textures Using Random Fields in a Random Environment, Pattern Recognition, vol. 32, June 1999, pp. 1009-1017.
[8] K. B. Eom, Segmentation of Monochrome and Color Textures Using Moving Average Modeling Approach, Image Vision Computing, vol. 3, no. 17, 1999, pp. 233–244.
[9] E. Oja and K. Valkealahti, Compressing HigherOrder Co-Occurrence for Texture Analysis Using the SelfOrganizing Map, Proceedings of the IEEE International Conference on Neural Networks, vol. 2, Nov.-Dec. 1995, pp. 1160-1164.
[10] Vision and Modeling Group, MIT Media Laboratory, Vision Texture (VisTex) database, http://wwwwhite.media.mit.edu/vismod/, 1995.
[11] Basic parameter values for the HDTV standard for the studio and for international programme exchange, Tech. Rep. ITU-R Recommendation BT.709, ITU, 1211 Geneva 20, Switzerland, 1990, formerly CCIR Rec. 709.
[12] J. W. Bennett, Modeling and Analysis of Gray Tone, Color, and Multispectral Texture Images by Random Field Models and Their Generalizations, Ph.D. Dissertation, Southern Methodist University, 1997.
[13] J. Bennett and A. Khotanzad, Maximum Likelihood Estimation Methods for Multispectral Random Field Image Models, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 21, no. 6, June 1999, pp. 537-543.
[14] S. Haykin, Neural Networks: Multilayer Perceptrons, (1994) 138-229, Macmillan College Publishing Company, NJ.
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Published
2005-10-03
How to Cite
Hernandez, O. J., Cook, J., Griffin, M., De Rama, C., & McGovern, M. (2005). Classification of color textures with random field models and neural networks. Journal of Computer Science and Technology, 5(03), p. 150–157. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/864
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