Texture Classification and Segmentation

  • Matti Pietikäinen
  • Abdenour Hadid
  • Guoying Zhao
  • Timo Ahonen
Part of the Computational Imaging and Vision book series (CIVI, volume 40)


The first part of this chapter provides an introduction to the most common texture image test sets and overviews some texture classification experiments involving LBP descriptors. An unsupervised method for texture segmentation using LBP and contrast (LBP/C) distributions is presented in the second part of the chapter. This method has become very popular, and many variants of it have been proposed, for example for color-texture segmentation and segmentation of remotely sensed images.


Texture Image Texture Classification Natural Scene Texture Segmentation Texture Boundary 


  1. 1.
    Ahonen, T., Pietikäinen, M.: Image description using joint distribution of filter bank responses. Pattern Recognit. Lett. 30(4), 368–376 (2009) CrossRefGoogle Scholar
  2. 2.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006) MATHGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001) MATHCrossRefGoogle Scholar
  4. 4.
    Brodatz, P.: Textures; A Photographic Album for Artists and Designers. Dover, New York (1966) Google Scholar
  5. 5.
    Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: Proc. International Conference on Computer Vision, pp. 1597–1604 (2005) Google Scholar
  6. 6.
    Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. ACM Trans. Graph. 18(1), 1–34 (1999) CrossRefGoogle Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001) MATHGoogle Scholar
  8. 8.
    Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997) MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the significance of real-world conditions for material classification. In: 8th European Conference on Computer Vision (ECCV 2004). Lecture Notes in Computer Science, vol. 3024, pp. 253–266. Springer, Berlin (2004) CrossRefGoogle Scholar
  10. 10.
    Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1265–1278 (2005) CrossRefGoogle Scholar
  11. 11.
    Lucieer, A., Stein, A., Fisher, P.: Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty. Int. J. Remote Sens. 26, 2917–2936 (2005) CrossRefGoogle Scholar
  12. 12.
    Mallikarjuna, P., Fritz, M., Targhi, A.T., Hayman, E., Caputo, B., Eklundh, J.-O.: The KTH-TIPS and KTH-TIPS2 databases, 2006. http://www.nada.kth.se/cvap/databases/kth-tips/
  13. 13.
    Mirmehdi, M., Xie, X., Suri, J. (eds.): Handbook of Texture Analysis. Imperial College Press, London (2008) Google Scholar
  14. 14.
    Ojala, T., Pietikäinen, M.: Unsupervised texture segmentation using feature distributions. In: International Conference on Image Analysis and Processing. Lecture Notes in Computer Science, vol. 1310, pp. 311–318. Springer, Berlin (1997) CrossRefGoogle Scholar
  15. 15.
    Ojala, T., Pietikäinen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recognit. 32, 477–486 (1999) CrossRefGoogle Scholar
  16. 16.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognit. 29(1), 51–59 (1996) CrossRefGoogle Scholar
  17. 17.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002) CrossRefGoogle Scholar
  18. 18.
    Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex—new framework for empirical evaluation of texture analysis algorithms. In: Proc. International Conference on Pattern Recognition, pp. 701–706 (2002) Google Scholar
  19. 19.
    Savelonas, M.A., Iakovidis, D.K., Maroulis, D.: LBP-guided active contours. Pattern Recognit. Lett. 29(9), 1404–1415 (2008) CrossRefGoogle Scholar
  20. 20.
    Sokal, R.R., Rohlf, F.J.: Biometry. Freeman, New York (1969) Google Scholar
  21. 21.
    Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991) CrossRefGoogle Scholar
  22. 22.
    Vapnik, V. (ed.): Statistical Learning Theory. Wiley, New York (1998) MATHGoogle Scholar
  23. 23.
    Varma, M., Zisserman, A.: Classifying images of materials: Achieving viewpoint and illumination independence. In: European Conference on Computer Vision. Lecture Notes in Computer Science, vol. 2352, pp. 255–271. Springer, Berlin (2002) Google Scholar
  24. 24.
    Varma, M., Zisserman, A.: Texture classification: Are filter banks necessary? In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 691–698 (2003) Google Scholar
  25. 25.
    Whelan, P.F., Ghita, O.: Colour texture analysis. In: Mirmehdi, M., Xie, X., Suri, J. (eds.) Handbook of texture analysis, pp. 129–163. Imperial College Press, London (2008) CrossRefGoogle Scholar
  26. 26.
    Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. Int. J. Comput. Vis. 73(2), 213–238 (2007) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Matti Pietikäinen
    • 1
  • Abdenour Hadid
    • 1
  • Guoying Zhao
    • 1
  • Timo Ahonen
    • 2
  1. 1.Machine Vision Group, Department of Computer Science and EngineeringUniversity of OuluOuluFinland
  2. 2.Nokia Research CenterPalo AltoUSA

Personalised recommendations