Feature Extraction Based on Co-occurrence of Adjacent Local Binary Patterns

  • Ryusuke Nosaka
  • Yasuhiro Ohkawa
  • Kazuhiro Fukui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7088)


In this paper, we propose a new image feature based on spatial co-occurrence among micropatterns, where each micropattern is represented by a Local Binary Pattern (LBP). In conventional LBP-based features such as LBP histograms, all the LBPs of micropatterns in the image are packed into a single histogram. Doing so discards important information concerning spatial relations among the LBPs, even though they may contain information about the image’s global structure. To consider such spatial relations, we measure their co-occurrence among multiple LBPs. The proposed feature is robust against variations in illumination, a feature inherited from the original LBP, and simultaneously retains more detail of image. The significant advantage of the proposed method versus conventional LBP-based features is demonstrated through experimental results of face and texture recognition using public databases.


Image feature extraction local binary pattern (LBP) co-occurrence face recognition texture recognition 


  1. 1.
    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)CrossRefzbMATHGoogle Scholar
  2. 2.
    Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition. In: Proc. IEEE International Conference on Computer Vision, vol. 1, pp. 786–791 (2005)Google Scholar
  3. 3.
    Zhao, G., Pietikäinen, M.: Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 915–928 (2007)Google Scholar
  4. 4.
    Lei, Z., Liao, S., He, R., Pietikainen, M., Li, S.: Gabor volume based local binary pattern for face representation and recognition. In: Proc. IEEE Conference on Automatic Face and Gesture Recognition, pp. 1–6 (2008)Google Scholar
  5. 5.
    Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing 19, 533–544 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29, 51–59 (1996)CrossRefGoogle Scholar
  7. 7.
    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, 2037–2041 (2006)CrossRefzbMATHGoogle Scholar
  8. 8.
    Watanabe, T., Ito, S., Yokoi, K.: Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection. In: Proc. the 3rd IEEE Pacific-Rim Symposium on Image and Video Technology, pp. 37–47 (2009)Google Scholar
  9. 9.
    Kobayashi, T., Otsu, N.: Image Feature Extraction Using Gradient Local Auto-Correlations. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 346–358. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Mita, T., Kaneko, T., Stenger, B., Hori, O.: Discriminative feature co-occurrence selection for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 1257–1269 (2008)CrossRefGoogle Scholar
  11. 11.
    Lee, K., Ho, J.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 684–698 (2005)CrossRefGoogle Scholar
  12. 12.
    Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11, 467–476 (2002)CrossRefGoogle Scholar
  13. 13.
    Swain Jr., M., Ballard, D.: Color indexing. International Journal of Computer Vision 7, 11–32 (1991)CrossRefGoogle Scholar
  14. 14.
    Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex - new framework for empirical evaluation of texture analysis algorithms. In: Proc. IEEE International Conference on Pattern Recognition, pp. 701–706 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ryusuke Nosaka
    • 1
  • Yasuhiro Ohkawa
    • 1
  • Kazuhiro Fukui
    • 1
  1. 1.Graduate School of Systems and Information EngineeringUniversity of TsukubaJapan

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