Rotationally Invariant Hashing of Median Binary Patterns for Texture Classification

  • Adel Hafiane
  • Guna Seetharaman
  • Kannappan Palaniappan
  • Bertrand Zavidovique
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)

Abstract

We present a novel image feature descriptor for rotationally invariant 2D texture classification. This extends our previous work on noise-resistant and intensity-shift invariant median binary patterns (MBPs), which use binary pattern vectors based on adaptive median thresholding. In this paper the MBPs are hashed to a binary chain or equivalence class using a circular bit-shift operator. One binary pattern vector (ie. smallest in value) from the group is selected to represent the equivalence class. The resolution and rotation invariant MBP (MBP ROT) texture descriptor is the distribution of these representative binary patterns in the image at one or more scales. A special subset of these rotation and scale invariant representative binary patterns termed uniform patterns leads to a more compact and robust MBP descriptor (MBP UNIF) that outperforms the rotation invariant uniform local binary patterns (LBP UNIF). We quantitatively compare and demonstrate the advantage of the new MBP texture descriptors for classification using the Brodatz and Outex texture dictionaries.

Keywords

Equivalence Class Local Binary Pattern Texture Descriptor Central Pixel Uniform Pattern 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tuceryan, M., Jain, A.K.: Texture analysis. Handbook of pattern recognition & computer vision, 235–276 (1993)Google Scholar
  2. 2.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6), 610–621 (1973)CrossRefGoogle Scholar
  3. 3.
    Davis, L.S., Johns, S.A., Aggarwal, J.K.: Texture analysis using generalized co-occurrence matrices. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(3), 251–259 (1979)CrossRefGoogle Scholar
  4. 4.
    Davis, L.S.: Polarograms: A new tool for image texture analysis. Pattern Recognition 13(3), 219–223 (1981)CrossRefGoogle Scholar
  5. 5.
    Kashyap, R., Khotanzad, A.: A model-based method for rotation invariant texture classification. PAMI 8, 472–481 (1986)Google Scholar
  6. 6.
    Mao, J., Jain, A.K.: Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition 25(2), 173–188 (1992)CrossRefGoogle Scholar
  7. 7.
    Cohen, F.S., Fan, Z., Patel, M.A.: Classification of rotated and scaled textured images using gaussian markov random field models. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(2), 192–202 (1991)CrossRefGoogle Scholar
  8. 8.
    Leung, M.M., Peterson, A.M.: Scale and rotation invariant texture classification. In: 26th Asilomar Conf Signals, Systems and Comp., pp. 461–465 (1992)Google Scholar
  9. 9.
    Porat, M., Zeevi, Y.Y.: The generalized Gabor scheme of image representation in biological and machine vision. IEEE Trans. PAMI 10(4), 452–468 (1988)MATHGoogle Scholar
  10. 10.
    Haley, G.M., Manjunath, B.S.: Rotation-invariant texture classification using a complete space-frequency model. IEEE Trans IP 8(2), 255–269 (1999)Google Scholar
  11. 11.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  12. 12.
    Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. International Journal of Computer Vision 62(1-2), 61–81 (2005)CrossRefGoogle Scholar
  13. 13.
    Hafiane, A., Seetharaman, G., Zavidovique, B.: Median binary pattern for textures classification. In: ICIAR, pp. 387–398 (2007)Google Scholar
  14. 14.
    Brodatz, P.: Texture: a Photographic Album for Artists and Designers. Dover, New York (1966)Google Scholar
  15. 15.
    Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllonen, J., Huovinene, S.: Outex - a new framework for empirical evaluation of texture analysis algorithms. In: Proc. 16th Intl. Conf. Pattern Recognition, vol. 1, pp. 706–707 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Adel Hafiane
    • 1
  • Guna Seetharaman
    • 2
  • Kannappan Palaniappan
    • 1
  • Bertrand Zavidovique
    • 3
  1. 1.Department of Computer ScienceUniversity of Missouri-ColumbiaColumbiaUSA
  2. 2.Department of Electrical and Computer EngineeringAir Force Institute of TechnologyDaytonUSA
  3. 3.Institut d’Electronique FondamentaleUniversité de Paris-SudOrsayFrance

Personalised recommendations