On the Robustness of Color Texture Descriptors across Illuminants

  • Simone Bianco
  • Claudio Cusano
  • Paolo Napoletano
  • Raimondo Schettini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)


In this paper we evaluate several extensions of Local Binary Patterns to color images. In particular, we investigate their robustness with respect to changes in the illuminant color temperature. To do so, we recovered the spectral reflectances of 1360 texture images from the Outex 13 data set. Then, we rendered the images as if they were taken under 33 different illuminants. For each combination of a training and test illuminant, we measured the classification performance of the texture features considered. The results of this extensive experimentation are reported and critically discussed.


Color texture classification illuminant invariance reflectance recovery Local Binary Patterns Outex texture database 


  1. 1.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. on PAMI 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  2. 2.
    Bianco, S.: Reflectance spectra recovery from tristimulus values by adaptive estimation with metameric shape correction. J. Opt. Soc. Am. A 27, 1868–1877 (2010)CrossRefGoogle Scholar
  3. 3.
    Chan, C.H., Kittler, J., Messer, K.: Multispectral local binary pattern histogram for component-based color face verification. In: First IEEE Intl. Conf. on Biometrics: Theory, Applications, and Systems, pp. 1–7 (2007)Google Scholar
  4. 4.
    Connah, D., Finlayson, G.: Using local binary pattern operators for colour constant image indexing. In: Proc. European Conf. on Color in Graphics, Imaging, and Vision, p. 5 (2006)Google Scholar
  5. 5.
    Cusano, C., Napoletano, P., Schettini, R.: Illuminant invariant descriptors for color texture classification. In: Tominaga, S., Schettini, R., Trémeau, A. (eds.) CCIW 2013. LNCS, vol. 7786, pp. 239–249. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Dupont, D.: Study of the reconstruction of reflectance curves based on tristimulus values: comparison of methods of optimization. Color Research and Application 27, 88–99 (2002)CrossRefGoogle Scholar
  7. 7.
    Finlayson, G.D., Drew, M.S., Funt, B.V.: Color constancy: Generalized diagonal transforms suffice. J. Opt. Soc. Am. A 11, 3011–3020 (1994)CrossRefGoogle Scholar
  8. 8.
    Haindl, M., Filip, J.: Visual Texture, vol. XXXI. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Mäenpää, T., Pietikäinen, M.: Classification with color and texture: jointly or separately? Pattern Recognition 37(8), 1629–1640 (2004)CrossRefGoogle Scholar
  10. 10.
    Mansouri, A., Sliwa, T., Hardeberg, J., Voisin, Y.: An adaptive-pca algorithm for reflectance estimation from color images. In: 19th Intl. Conf. on Pattern Recognition, pp. 1–4 (2008)Google Scholar
  11. 11.
    Mirmehdi, M., Xie, X., Suri, J.: Handbook of Texture Analysis. Imperial College Press, London (2008)CrossRefGoogle Scholar
  12. 12.
    Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artificial Intelligence in Medicine 49(2), 117–125 (2010)CrossRefGoogle Scholar
  13. 13.
    Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex-new framework for empirical evaluation of texture analysis algorithms. In: 16th Intl. Conf. on Pattern Recognition, vol. 1, pp. 701–706 (2002)Google Scholar
  14. 14.
    Ojala, T., Pietikäinen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recognition 32(3), 477–486 (1999)CrossRefGoogle Scholar
  15. 15.
    Ojala, T., Pietikäinen, M., Mänepää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  16. 16.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Local binary patterns for still images. In: Computer Vision Using Local Binary Patterns, Computational Imaging and Vision, vol. 40, pp. 13–47. Springer, London (2011)CrossRefGoogle Scholar
  17. 17.
    Porebski, A., Vandenbroucke, N., Macaire, L.: Haralick feature extraction from lbp images for color texture classification. In: First Workshops on Image Processing Theory, Tools and Applications, pp. 1–8 (2008)Google Scholar
  18. 18.
    Vhrel, M., Gershon, R., Iwan, L.: Measurement and analysis of object reflectance spectra. Color Research and Application, 4–9 (1994)Google Scholar
  19. 19.
    Zhu, C., Bichot, C.E.: Multi-scale color local binary patterns for visual object classes recognition. In: ICB 2007, pp. 3065–3068 (2010)Google Scholar
  20. 20.
    Zuffi, S., Santini, S., Schettini, R.: From color sensor space to feasible reflectance spectra. IEEE Trans. on Signal Processing 56, 518–531 (2008)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Simone Bianco
    • 1
  • Claudio Cusano
    • 2
  • Paolo Napoletano
    • 1
  • Raimondo Schettini
    • 1
  1. 1.DISCo (Dipartimento di Informatica, Sistemistica e Comunicazione)Università degli Studi di Milano-BicoccaMilanoItaly
  2. 2.Università degli Studi di PaviaPaviaItaly

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