Avoiding the Curse of Dimensionality in Local Binary Patterns

  • Karel PetranekEmail author
  • Jan Vanek
  • Eva Milkova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9875)


Local Binary Patterns is a popular grayscale texture operator used in computer vision for classifying textures. The output of the operator is a bit string of a defined length, usually 8, 16 or 24 bits, describing local texture features. We focus on the problem of succinctly representing the patterns using alternative means and compressing them to reduce the number of dimensions. These reductions lead to simpler connections of Local Binary Patterns with machine learning algorithms such as neural networks or support vector machines, improve computation speed and simplify information retrieval from images. We study the distribution of Local Binary Patterns in 100000 natural images and show the advantages of our reduction technique by comparing it to existing algorithms developed by Ojala et al. We have also confirmed Ojala’s findings about the uniform LBP proportions.


Dimensionality reduction Local binary patterns Image analysis 



This paper was supported by the research project SPEV, University of Hradec Kralove, Faculty of Informatics and Management, 2016.


  1. 1.
    Ojala, T., Pietikäinen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recognit. 32, 477–486 (1999)CrossRefGoogle Scholar
  2. 2.
    Qing, X., Jie, Y., Siyi, D.: Texture segmentation using LBP embedded region competition. Electron. Lett. Comput. Vis. Image Anal. 5, 41–47 (2005)Google Scholar
  3. 3.
    Rara, H., Farag, A., Elhabian, S., Ali, A., Miller, W., Starr, T., Davis, T.: Face recognition at-a-distance using texture and sparse-stereo reconstruction. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–6. IEEE (2010)Google Scholar
  4. 4.
    Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recognit. Lett. 33, 431–437 (2012)CrossRefGoogle Scholar
  5. 5.
    Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 32–39. IEEE (2009)Google Scholar
  6. 6.
    Trefný, J., Matas, J.: Extended set of local binary patterns for rapid object detection. In: Proceedings of the Computer Vision Winter Workshop (2010)Google Scholar
  7. 7.
    Chang, T., Kuo, C.-C.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2, 429–441 (1993)CrossRefGoogle Scholar
  8. 8.
    Livens, S., Scheunders, P., Van de Wouwer, G., Van Dyck, D.: Wavelets for texture analysis, an overview. In: Sixth International Conference on Image Processing and Its Applications, 1997, pp. 581–585. IET (1997)Google Scholar
  9. 9.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Gray scale and rotation invariant texture classification with local binary patterns. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 404–420. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)CrossRefzbMATHGoogle Scholar
  11. 11.
    Pietikäinen, M., Ojala, T., Xu, Z.: Rotation-invariant texture classification using feature distributions. Pattern Recognit. 33, 43–52 (2000)CrossRefGoogle Scholar
  12. 12.
    Guo, Z., Zhang, D., Mou, X.: Hierarchical multiscale LBP for face and palmprint recognition. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 4521–4524. IEEE (2010)Google Scholar
  13. 13.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. ArXiv Prepr. arXiv:14090575. (2014)
  14. 14.
    Lecun, Y., Cortes, C.: The MNIST database of handwritten digits.
  15. 15.
    Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Comput. Sci. Dep. Univ. Tor. Technical Report 1, 7 (2009)Google Scholar
  16. 16.
    Chollet, F.: Keras Deep Learning Framework. GitHub (2015)Google Scholar
  17. 17.
  18. 18.
    Liu, L., Zhao, L., Long, Y., Kuang, G., Fieguth, P.: Extended local binary patterns for texture classification. Image Vis. Comput. 30, 86–99 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of Hradec KraloveHradec KraloveCzech Republic

Personalised recommendations