Probabilistic Rules for Automatic Texture Segmentation

  • Justino Ramírez
  • Mariano Rivera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


We present an algorithm for automatic selection of features that best segment an image in texture homogeneous regions. The set of “best extractors” are automatically selected among the Gabor filters, Co-occurrence matrix, Law’s energies and intensity response. Noise-features elimination is performed by taking into account the magnitude and the granularity of each feature image, i.e. the compute image when a specific feature extractor is applied. Redundant features are merged by means of probabilistic rules that measure the similarity between a pair of image feature. Then, cascade applications of general purpose image segmentation algorithms (K-Means, Graph-Cut and EC-GMMF) are used for computing the final segmented image. Additionally, we propose an evolutive gradient descent scheme for training the method parameters for a benchmark image set. We demonstrate by experimental comparisons, with stat of the art methods, a superior performance of our technique.


Feature Selection Image Segmentation Association Rule Gabor Filter Texture Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Cogging, J.M.: A Framework for texture analysis Based on Spatial Filtering. Ph.D. Thesis, Computer Science Department, Michigan State University, East Lansing, MI (1982)Google Scholar
  2. 2.
    Skalansky, J.: Image Segmentation and Feature Extraction. IEEE Trans. Syst. Man Cybern., 237–247 (1978)Google Scholar
  3. 3.
    Kam, A.H., Fitzgerald, W.J.: Unsupervised Multiscale Image Segmentation. In: Proceedings of 10th International Conference on Image Analysis and Processing (ICIAP 1999), pp. 237–247 (1999)Google Scholar
  4. 4.
    Chang, K.I., Bowyer, K.W., Sivagurunath, M.: Evaluation of Texture Segmentation Algorithms. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, Fort Collins, USA, pp. 294–299 (1999)Google Scholar
  5. 5.
    Polk, G., Liu, J.S.: Texture Analysis by a Hybrid Scheme. Store, Storage and Retrieval for Image and Video Databases VII, 614–622 (1998)Google Scholar
  6. 6.
    Clausi, D.A., Deng, H.: Design-Based Textural Feature Fusion Using Gabor Filters and Co-Occurrence Probabilities. IEEE Trans. on Image Processing, 925–936 (2005)Google Scholar
  7. 7.
    Liu, C., Wechsler, H.: Independent Component Analysis of Gabor Features for Face Recognition. IEEE Trans. on Neural Networks, 919–928 (2003)Google Scholar
  8. 8.
    Guyon, I., Elisseeff, A.: An Introduction to Feature and Variable Selection. Journal of Machine Learning Research, 1157–1182 (2003)Google Scholar
  9. 9.
    Jain, A.K., Farrokhnia, F.: Unsupervised Texture Segmentation Using Gabor Filters. Pattern Recognition, 1167–1186 (1991)Google Scholar
  10. 10.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. Syst. Man. Cybern. 28, SMC3(6), 610–621 (1973)CrossRefGoogle Scholar
  11. 11.
    Laws, K.: Texture Image Segmentation. Ph.D. Dissertation, University of Southern California (1980)Google Scholar
  12. 12.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Machine Intel. 12, 629–639 (1990)CrossRefGoogle Scholar
  13. 13.
    Huber, P.J.: Robust Statistics. John Wiley and Sons, New York (1981)MATHCrossRefGoogle Scholar
  14. 14.
    Rivera, M., Ocegeda, O., Marroquin, J.L.: Entropy Controlled Gauss-Markov Random Measure fields for early vision. In: Paragios, N., Faugeras, O., Chan, T., Schnörr, C. (eds.) VLSM 2005. LNCS, vol. 3752, pp. 137–148. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proceedings of ACM SIGMOD, pp. 207–216 (1993)Google Scholar
  16. 16.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Advance Reference Series. Prentice-Hall, Englewood Cliffs (1988)MATHGoogle Scholar
  17. 17.
    Veksler, O.: Efficient Graph-based Energy Minimization Methods in Computer Vision, PhD. Thesis, Cornell University (1999)Google Scholar
  18. 18.
    Min, J., Powell, M., Bowyer, K.W.: Automated Performance Evaluation of Range Image Segmentation Algorithms. IEEE Trans. On Systems, man, and cybernetics-Part B 34(1), 263–271 (2004)CrossRefGoogle Scholar
  19. 19.
    Brodatz, P.: Texture –A Photographic Album for Artists and designers. Reinhold, New York (1968)Google Scholar
  20. 20.
    Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, New York (2001)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Justino Ramírez
    • 1
  • Mariano Rivera
    • 1
  1. 1.Centro de Investigación en Matemáticas A.C. (CIMAT)Guanajuato, Gto.México

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