Perceptual smoothing and segmentation of colour textures

  • M. Petrou
  • M. Mirmehdi
  • M. Coors
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1406)


An approach for perceptual segmentation of colour image textures is described. A multiscale representation of the texture image, generated by a multiband smoothing algorithm based on human psychophysical measurements of colour appearance is used as the input. Initial segmentation is achieved by applying a clustering algorithm to the image at the coarsest level of smoothing. Using these isolated core clusters 3D colour histograms are formed and used for probabilistic assignment of all other pixels to the core clusters to form larger clusters and categorise the rest of the image. The process of setting up colour histograms and probabilistic reassignment of the pixels is then propagated through finer levels of smoothing until a full segmentation is achieved at the highest level of resolution.


Texture Image Markov Random Field Colour Histogram Coarse Level Core Cluster 
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.


  1. 1.
    X. Zhang and B.A. Wandell. A spatial extension of CIELAB for digital color image reproduction. In Society for Information Display Symposium, San Diego, 1996. WWW address: Scholar
  2. 2.
    B.A. Wandell and X. Zhang. SCIELAB: a metric to predict the discriminability of colored patterns. In 9th Workshop on Image and Multidimensional Signal Processing, pages 11–12, 1996.Google Scholar
  3. 3.
    S. Peleg, J. Naor, R. Hartley, and D. Avnir. Multiple resolution texture analysis and classification. IEEE Trans. Pattern Analysis and Machine Intelligence, 6(4):518–523, 1984.Google Scholar
  4. 4.
    J.L. Crowley and A.C. Sanderson. Multiple resolution representation and probabilistic matching of 2-d gray-scale shape. IEEE Trans. Pattern Analysis and Machine Intelligence, 9:113–120, 1987.CrossRefGoogle Scholar
  5. 5.
    C. Bouman and B. Liu. Multiple resolution segmentation of textured images. IEEE Trans. Pattern Analysis and Machine Intelligence, 13(2):99–113, 1991.CrossRefGoogle Scholar
  6. 6.
    S. Lam and H. Ip. Structural texture segmentation using irregular pyramid. Pattern Recognition Letters, 15:691–698, 1994.CrossRefGoogle Scholar
  7. 7.
    F. Glazer. Multilevel relaxation in low-level computer vision. In A. Rosenfeld, editor, Multiresolution Image Processing and Analysis, pages 312–330. Springer, 1984.Google Scholar
  8. 8.
    D. Terzopoulos. Image analysis using multigrid relaxation methods. IEEE Trans. Pattern Analysis and Machine Intelligence, 8(2):129–139, 1986.Google Scholar
  9. 9.
    D. Zhang, J. Liu, and F. Wan. Multiresolution relaxation: Experiments and evaluations. In Proceedings of International Conference on Pattern Recognition, pages 712–714, 1988.Google Scholar
  10. 10.
    E. R. Hancock, M. Haindl, and J. Kittler. Multiresolution edge labelling using hierarchical relaxation. In Proceedings of International Conference on Pattern Recognition, pages 140–144, 1992.Google Scholar
  11. 11.
    J. Matas and J. Kittler. Spatial and feature based clustering: Applications in image analysis. In CAIP95, pages 162–173, 1995.Google Scholar
  12. 12.
    B. Julesz and J.R. Bergen. Textons, the fundamental elements in preattentive vision and perception of textures. Bell Systems Technical Journal, 62(6):1619–1645, 1983.Google Scholar
  13. 13.
    J. Malik and P. Perona. A computational model of texture perception. Technical Report CSD-89-491, University of California Berkeley, CS, 1989.Google Scholar
  14. 14.
    H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. SMC, 8(6):460–473, June 1978.Google Scholar
  15. 15.
    R. M. Haralick. Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5):786–804, 1979.CrossRefGoogle Scholar
  16. 16.
    L. V. Gool, P. Dewaele, and A. Oosterlinck. Texture analysis anno 1983. Computer Vision, Graphics and Image Processing, 29:336–357, 1985.CrossRefGoogle Scholar
  17. 17.
    T.R. Reed and J. du Buf. A review of recent texture segmentation and feature extraction techniques. CVGIP: Image Understanding, 57:359–372, 1993.CrossRefGoogle Scholar
  18. 18.
    A.K. Jain and F. Farrokhnia. Unsupervised texture segmentation using gabor filters. Pattern Recognition, 24(12):1167–1186, 1991.CrossRefGoogle Scholar
  19. 19.
    M. Unser and M. Eden. Multiresolution feature extraction and selection for texture segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence, 11(7):717–728, July 1989.CrossRefGoogle Scholar
  20. 20.
    I. Matalas, S. Roberts, and H. Hatzakis. A set of multiresolution texture features suitable for unsupervised image segmentation. In Proceedings of Signal Processing VIII, Theories and Applications, volume III, pages 1495–1498, 1996.Google Scholar
  21. 21.
    S.J. Roan, J.K. Aggarwal, and W.N. Martin. Multiple resolution imagery and texture analysis. Pattern Recognition, 20(1):17–31, 1987.CrossRefGoogle Scholar
  22. 22.
    Y. Ohta, T. Kanade, and T. Sakai. Color information for region segmentation. Computer Graphics and Image Processing, 13:222–241, 1980.CrossRefGoogle Scholar
  23. 23.
    G. Healey. Segmenting images using normalized color. IEEE Trans. Systems, Man, and Cybernetics, 22(1):64–73, 1992.MathSciNetCrossRefGoogle Scholar
  24. 24.
    J. Liu and Y-H. Yang. Multiresolution color image segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence, 16:689–700, July 1994.CrossRefGoogle Scholar
  25. 25.
    W. Skarbek and A. Koschan. Colour image segmentation — a survey. Technical report, Technical University Berlin, 1994.Google Scholar
  26. 26.
    M.J. Swain. Color Indexing. PhD thesis, University of Rochester, 1990.Google Scholar
  27. 27.
    G. J. Klinker. A Physical Approach to Color Image Understanding. A K Peters, Wellesley, Massachusetss, 1993.zbMATHGoogle Scholar
  28. 28.
    G. B. Coleman and H. C. Andrews. Image segmentation by clustering. Proceedings of the IEEE, 67(5):773–785, 1979.Google Scholar
  29. 29.
    S. C. Tan and J. Kittler. Colour texture classification using features from colour histogram. Proceedings of the Eighth Scandinavian Conference on Image Processing, 1993.Google Scholar
  30. 30.
    D.K. Panjwani and G. Healey. Unsupervised segmentation of textured color images using markov random field models. In Conference on Computer Vision and Pattern Recognition, pages 776–777, 1993.Google Scholar
  31. 31.
    L. Shafarenko, M. Petrou, and J. Kittler. Automatic watershed segmentation of randomly textured color images. IEEE Transactions on Image Processing, 6(11):1530–1544, November 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • M. Petrou
    • 1
  • M. Mirmehdi
    • 1
  • M. Coors
    • 1
  1. 1.Centre for Vision, Speech and Signal ProcessingSurrey UniversityGuildfordEngland

Personalised recommendations