Abstract
In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian estimation associated with combinatorial optimization (Simulated Annealing). The segmentation is obtained by classifying the pixels into different pixel classes. These classes are represented by multi-variate Gaussian distributions. Thus, the only hypothesis about the nature of the features is that an additive white noise model is suitable to describe the feature values belonging to a given class. Herein, we use the perceptually uniform CIE-L*u*v* color values as color features and a set of Gabor filters as texture features. We provide experimental results that illustrate the performance of our method on both synthetic and natural color images. Due to the local nature of our MRF model, the algorithm can be highly parallelized.
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References
Besag, J.: On the statistical analysis of dirty pictures. J. Roy. Statist. Soc., ser. B, 1986
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence 6 (1984) 721–741
Huang, C.L., Cheng, T.Y., Chen, C.C.: Color images segmentation using scale space filter and Markov random field. Pattern Recognition 25(10) (1992) 1217–1229
Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recognition 24(12) (1991) 1167–1186
Kato, Z., Zerubia, J., Berthod, M.: Unsupervised parallel image classification using Markovian models. Pattern Recognition 32(4) (1999) 591–604
Liu, J., Yang, Y.H.: Multiresolution color image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 16(7) (July) (1994) 689–700
Panjwani, D.K., Healey, G.: Markov random field models for unsupervised segmentation of textured color images. IEEE Trans. on Pattern Analysis and Machine Intelligence 17(10) (October) (1995) 939–954
Randen, T., Husoy, J.H.: Filtering for texture classification: A comparative study. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(4) (April) (1999) 291–310
Sangwine, S.J., Horne, R.E.N. (eds): The Colour Image Processing Handbook. Chapman & Hall (1998)
Won, C.S., Derin, H.: Unsupervised segmentation of noisy and textured images using Markov random fields. Computer Graphics and Image Processing: Graphical Models and Image Processing 54(4) (July) (1992) 208–328
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© 2001 Springer-Verlag Berlin Heidelberg
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Kato, Z., Pong, TC. (2001). A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features. In: Skarbek, W. (eds) Computer Analysis of Images and Patterns. CAIP 2001. Lecture Notes in Computer Science, vol 2124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44692-3_66
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DOI: https://doi.org/10.1007/3-540-44692-3_66
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