Gabor Kernels for Textured Image Representation and Classification

  • Hugo Hidalgo-Silva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


A Gabor based representation for textured images is proposed. Instead of the ordinary filter bank, a reproducing kernel representation is constructed consisting of a sum of several local reproducing kernels. The image representation coefficients are computed by a basis pursuit procedure, and are then considered as the feature vectors. The feature vectors are used to construct a kernel for a support vector classifier. Results are presented for a set of oriented texture images.


Feature Vector Texture Image Image Representation Reproduce Kernel Hilbert Space Kernel Representation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Randen, T., Håkon Husøy, J.: Filtering for Texture Classification: A Comparative Study. IEEE Tran. on Pattern Analysis and Machine Intelligence 21, 291–310 (1989)CrossRefGoogle Scholar
  2. 2.
    Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor Filters. Pattern Recognition 24, 1167–1186 (1990)CrossRefGoogle Scholar
  3. 3.
    Teuner, A., Pichler, O., Hosticka, B.J.: Unsupervised texture segmentation of images using tuned matched Gabor filters. IEEE Trans. Image Process 4, 863–870 (1995)CrossRefGoogle Scholar
  4. 4.
    Haralick, R.: Statistical and Structural Approaches to Texture. Proceedings of the IEEE 67, 786–804 (1979)CrossRefGoogle Scholar
  5. 5.
    Varma, M., Zisserman, A.: A statistical Approach to Texture Classification from Single Images. International Journal of Computer Vision 62, 61–81 (2005)Google Scholar
  6. 6.
    Clausi, D., Jernigan, M.: Designing Gabor filters for optimal texture separability. Pattern Recognition 33, 1835–1849 (2000)CrossRefGoogle Scholar
  7. 7.
    Chui, C.K.: An Introduction to Wavelets. Academic Press, San Diego (1992)MATHGoogle Scholar
  8. 8.
    Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar
  9. 9.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)Google Scholar
  10. 10.
    Máté, L.: Hilbert Space Methods in Science and Engineering. Adam Hilger, Bristol (1989)MATHGoogle Scholar
  11. 11.
    Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic Decomposition by Basis Pursuit. SIAM Review 43, 129–159 (2001)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Czyzyk, J., Mehrotra, S., Wagner, M., Wright, S.: PCx User Guide, Optimization Technology Center, Technical Report OTC 96/01 (1997)Google Scholar
  13. 13.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1966)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hugo Hidalgo-Silva
    • 1
  1. 1.CICESE-Ciencias de la ComputaciónEnsenadaMéxico

Personalised recommendations