Advertisement

Incorporating Texture Intensity Information into LBP-Based Operators

  • M. Ghahramani
  • Guoying Zhao
  • Matti Pietikäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

In this paper, we aim to improve the accuracy of LBP-based operators by including texture image intensity characteristics in the operator. We utilize shifted step function to minimize the quantization error of the step function to obtain more discriminative operators. Features obtained from shifted step function are simply fused together to form the final histogram. This model is generalized and can be integrated with other existing LBP variants to reduce quantization error of the step function for texture classification. The proposed method is integrated with multiple LBP-based feature descriptors and evaluated on publicly available texture databases (Outex_TC_00012 and KTH-TIPS2b) for texture classification. Experimental results demonstrate that it not only improves the performance of operators it is integrated with but also achieves higher accuracy compared to the state of the art in texture classification.

Keywords

Texture LBP Intensity information 

References

  1. 1.
    Tuceryan, M., Jain, A.K.: Texture Analysis. In: Chen, C.H., et al. (eds.) Handbook of Pattern Recognition and Computer Vision. World Scientific (1993)Google Scholar
  2. 2.
    Zhang, J., Tan, T.: Brief Review of Invariant Texture Analysis Methods. Pattern Recognition 35, 735–747 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  4. 4.
    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, 971–987 (2002)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.
    Mäenpää, T., Pietikäinen, M.: Multi-scale Binary Patterns for Texture Analysis. In: Proceedings of the 13th Scandinavian Conference on Image Analysis, Halmstad, Sweden (2003)Google Scholar
  7. 7.
    Guo, Z., Zhang, L., Zhang, D.: Rotation Invariant Texture Classification Using LBP Variance (LBPV) with Global Matching. Pattern Recognition 43, 706–719 (2010)zbMATHCrossRefGoogle Scholar
  8. 8.
    Guo, Z., Zhang, L., Zhang, D.: A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Transactions on Image Processing 19, 1657–1663 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zhao, G., Ahonen, T., Matas, J., Pietikäinen, M.: Rotation-Invariant Image and Video Description With Local Binary Pattern Features. IEEE Trans. on Image Processing 21, 1465–1477 (2012)CrossRefGoogle Scholar
  10. 10.
    Ghahramani, M., Yau, W.Y., Teoh, E.K.: Enhancing Local Binary Patterns Distinctiveness for Face Representation. In: 2011 IEEE International Symposium on Multimedia (ISM), pp. 440–445 (2011)Google Scholar
  11. 11.
    Guo, Y., Zhao, G., Pietikäinen, M.: Discriminative Features for Texture Description. Pattern Recognition (2012)Google Scholar
  12. 12.
    Tan, X., Triggs, B.: Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. IEEE Transactions on Image Processing 19, 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Varma, M., Zisserman, A.: A Statistical Approach to Material Classification Using Image Patch Exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 2032–2047 (2009)CrossRefGoogle Scholar
  14. 14.
    Ojala, T., et al.: Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms. In: Proceedings of the 16th International Conference on Pattern Recognition (ICPR 2002), vol. 1 (2002)Google Scholar
  15. 15.
    Caputo, B., Hayman, E., Mallikarjuna, P.: Class-Specific Material Categorisation. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 2 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • M. Ghahramani
    • 1
  • Guoying Zhao
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Center for Machine Vision Research, Department of Computer Science and EngineeringUniversity of OuluFinland

Personalised recommendations