Learning an Efficient Texture Model by Supervised Nonlinear Dimensionality Reduction Methods

  • Elnaz Barshan
  • Mina Behravan
  • Zohreh Azimifar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


This work investigates the problem of texture recognition under varying lighting and viewing conditions. One of the most successful approaches for handling this problem is to focus on textons, describing local properties of textures. Leung and Malik [1] introduced the framework of this approach which was followed by other researchers who tried to address its limitations such as high dimensionality of textons and feature histograms as well as poor classification of a single image under known conditions.

In this paper, we overcome the above-mentioned drawbacks by use of recently introduced supervised nonlinear dimensionality reduction methods. These methods provide us with an embedding which describes data instances from the same classes more closely to each other while separating data from different classes as much as possible. Here, we take advantage of the superiority of modified methods such as “Colored Maximum Variance Unfolding” as one of the most efficient heuristics for supervised dimensionality reduction.

The CUReT (Columbia-Utrecht Reflectance and Texture) database is used for evaluation of the proposed method. Experimental results indicate that the algorithm we have put forward intelligibly outperforms the existing methods. In addition, we show that intrinsic dimensionality of data is much less than the number of measurements available for each item. In this manner, we can practically analyze high dimensional data and get the benefits of data visualization.


Texture Recognition Texton Dimensionality Reduction 


  1. 1.
    Leung, T.K., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43(1), 29–44 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. International Journal of Computer Vision 62(1)Google Scholar
  3. 3.
    Cula, O.G., Dana, K.J.: 3d texture recognition using bidirectional feature histograms. International Journal of Computer Vision 59(1), 33–60 (2004)CrossRefGoogle Scholar
  4. 4.
    Clark, M., Bovik, A.C., Geisler, W.S.: Multichannel texture analysis using localized spatial filters. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 55–73 (1990)CrossRefGoogle Scholar
  5. 5.
    Randen, T., Husoy, J.H.: Filtering for texture classification: A comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 291–310 (1999)CrossRefGoogle Scholar
  6. 6.
    Prabhakar, S., Jain, A.K., Hong, L.: A multichannel approach to fingerprint classification. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 348–359 (1999)CrossRefGoogle Scholar
  7. 7.
    Smola, A.J., Borgwardt, K.M., Song, L., Gretton, A.: Colored maximum variance unfolding. In: NIPS (2007)Google Scholar
  8. 8.
    Weinberger, K.Q., Saul, L.K.: An introduction to nonlinear dimensionality reduction by maximum variance unfolding. In: AAAI (2006)Google Scholar
  9. 9.
    Bousquet, O., Smola, A.J., Gretton, A., Scholköpf, B.: Measuring statistical dependence with hilbert-schmidt norms. In: ALT, pp. 63–77 (2005)Google Scholar
  10. 10.
    Saul, L.K., Roweis, S.T.: Think globally, fit locally: Unsupervised learning of low dimensional manifold. Journal of Machine Learning Research 14, 119–155 (2003)Google Scholar
  11. 11.
    Nayar, S.K., Koenderink, J.J., Dana, K.J., Ginneken, B.v.: Reflectance and texture of real-world surfaces. ACM Trans. Graph. 18(1), 1–34 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Elnaz Barshan
    • 1
  • Mina Behravan
    • 1
  • Zohreh Azimifar
    • 1
  1. 1.School of Electrical and Computer EngineeringShiraz UniversityShirazIran

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