Texture Description by Independent Components

  • Dick de Ridder
  • Robert P. W. Duin
  • Josef Kittler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


A model for probabilistic independent component subspace analysis is developed and applied to texture description. Experiments show it to perform comparably to a Gaussian model, and to be useful mainly for problems in which the detection of little occurring, high-frequency image elements is important.


Independent Component Analysis Independent Component Gaussian Model Texture Image Texture Description 
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.
    P. Brodatz. Textures, a photographic album for artists and designers. Dover Publications, New York, NY, 1966.Google Scholar
  2. 2.
    D. de Ridder. Adaptive methods of image processing. PhD thesis, Delft University of Technology, Delft, 2001.Google Scholar
  3. 3.
    D. de Ridder, J. Kittler, O. Lemmers, and R.P.W. Duin. The adaptive subspace map for texture segmentation. In Proc. ICPR 2000, pages216–220, Los Alamitos, CA, 2000. IAPR, IEEE Computer Society Press.Google Scholar
  4. 4.
    D. de Ridder, O. Lemmers, R.P.W. Duin, and J. Kittler. The adaptive subspace map for image description and image database retrieval. In Proc. S+SSPR 2000, pages 94–103, Berlin, 2000. IAPR, Springer-Verlag.Google Scholar
  5. 5.
    K. Fukunaga. Introduction to statistical pattern recognition. Electrical Science Series. Academic Press, NY, NY, 1972.Google Scholar
  6. 6.
    J. Hurri. Independent component analysis of image data. Master’s thesis, Dept. of Computer Science and Engineering, Helsinki University of Technology, Espoo, Finland, March 1997.Google Scholar
  7. 7.
    A. Hyvärinen. Survey on independent component analysis. Neural Computing Surveys, 1(2):94–128, 1999.Google Scholar
  8. 8.
    T. Kohonen, S. Kaski, and H. Lappalainen. Self-organized formation of various invariant-feature filters in the Adaptive-Subspace SOM. Neural Computation, 9(6):1321–1344, 1997.CrossRefGoogle Scholar
  9. 9.
    T.-W. Lee, M. Girolami, and T.J. Sejnowski. Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources. Neural Computation, 11(2):417–441, 1999.CrossRefGoogle Scholar
  10. 10.
    H. Lu, Y. Fainman, and R. Hecht-Nielsen. Image manifolds. In Applications of Artificial Neural Networks in Image Processing III, Proceedings of SPIE, volume3307, pages 52–63, Bellingham, WA, 1998. SPIE, SPIE.Google Scholar
  11. 11.
    M.E. Tipping and C.M. Bishop. Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2):443–482, 1999.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Dick de Ridder
    • 1
  • Robert P. W. Duin
    • 1
  • Josef Kittler
    • 2
  1. 1.Pattern Recognition Group Dept. of Applied Physics, Faculty of Applied SciencesDelft University of TechnologyDelftThe Netherlands
  2. 2.Centre for Vision, Speech and Signal Processing School of Electronics, Computing and MathematicsUniversity of Surrey GuildfordSurreyUK

Personalised recommendations