Texture Description with Completed Local Quantized Patterns

  • Xiaohua Huang
  • Guoying Zhao
  • Xiaopeng Hong
  • Matti Pietikäinen
  • Wenming Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

Local binary patterns (LBP) has been very successful in a number of areas, including texture analysis and face analysis. Recently, local quantized patterns (LQP) was proposed to use vector quantization to code complicated patterns with a large number of neighbors and several quantization levels. It uses lookup table technique to map patterns into the corresponding indices. In this paper, we propose completed local quantized patterns (CLQP) for improving the performance of LQP. Firstly, we find that LQP only considers the sign-based difference, it thus misses some discriminative information. We therefore propose to use the magnitude-based and orientation-based differences to complement the sign-based difference for LQP. We finally use vector quantization to learn three separate codebooks for local sign, magnitude and orientation patterns, respectively. Secondly, we also observe that LQP uses random initialization in vector quantization, this leads to losing the distribution of local patterns and costing much computational time. For reducing the unnecessary computational time of initialization, we use preselected dominant patterns as the initialization. Our experimental results show that CLQP outperforms well-established features including LBP, LTP, CLBP, LQP on a range of challenging texture classification problems and an infant pain detection problem.

Keywords

Local binary pattern local orientation magnitude texture descriptor pain detection 

References

  1. 1.
    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
  2. 2.
    Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: IEEE International Conference on Computer Vision, pp. 1597–1602. IEEE Press, New York (2005)Google Scholar
  3. 3.
    Chen, J., Shan, C., He, C., Zhao, G., Pietikäinen, M., Chen, X.: WLD: A robust local image descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1705–1720 (2010)CrossRefGoogle Scholar
  4. 4.
    Dana, K., Ginneken, B., Nayar, S., Koenderink, J.: Reflectance and texture of real-world surfaces. ACM Transactions on Graphics 18(1), 1–34 (1999)CrossRefGoogle Scholar
  5. 5.
    Gizatdinova, Y., Surakka, V.: Feature-based detection of facial landmarks from neutral and expressive facial images. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 135–139 (2006)CrossRefGoogle Scholar
  6. 6.
    Guo, Y., Zhao, G., Pietikäinen, M.: Discriminative features for texture description. Pattern Recognition 45(10), 3834–3843 (2012)CrossRefGoogle Scholar
  7. 7.
    Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing 19(6), 1657–1663 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hussain, S.u., Triggs, B.: Visual recognition using local quantized patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 716–729. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Liao, S., Law, W., Chung, C.: Dominant local binary patterns for texture classification. IEEE Transactions on Image Processing 18(5), 1107–1118 (2009)CrossRefGoogle Scholar
  10. 10.
    Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognition 42(3), 425–436 (2009)MATHCrossRefGoogle Scholar
  11. 11.
    Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artificial Intelligence in Medicine 49(2), 117–125 (2010)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Ojala, T., Valkealahti, K., Oja, E., Pietikäinen, M.: Texture discrimination with multidimensional distributions of signed gray level differences. Pattern Recognition 34(3), 727–739 (2001)MATHCrossRefGoogle Scholar
  14. 14.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision using Local Binary Patterns. Springer, Berlin (2011)CrossRefGoogle Scholar
  15. 15.
    Tan, X., Triggs, B.: Enhanced local texture sets for face reocgnition under difficult lighting conditions. IEEE Transactions on Image Processing 19(6), 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: Subspace learning from image gradient orientations. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(12), 2454–2466 (2012)CrossRefGoogle Scholar
  17. 17.
    Varma, M., Zisserman, A.: Classifying images of materials: Achieving viewpoint and illumination independence. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 255–271. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Vu, N.G., Caplier, A.: Mining patterns of orientations and magnitudes for face recognition. In: International Joint Conference on Biometrics, pp. 1–8. IEEE Press, New York (2011)Google Scholar
  19. 19.
    Xie, S., Shan, S., Chen, X., Chen, J.: Fusing local patterns of Gabor magnitude and phase for face recognition. IEEE Transaction on Image Processing 19(5), 1349–1361 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Zhang, B., Shan, C., Chen, X., Gao, W.: Histogram of gabor phase patterns (HGPP): A novel object representation approach for face recognition. IEEE Transactions on Image Processing 16(1), 57–68 (2007)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Zhao, G., Ahonen, T., Matas, J., Pietikäinen, M.: Rotation invariant image and video description with local binary pattern features. IEEE Transactions on Image Processing 21(4), 1465–1467 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaohua Huang
    • 1
  • Guoying Zhao
    • 1
  • Xiaopeng Hong
    • 1
  • Matti Pietikäinen
    • 1
  • Wenming Zheng
    • 2
  1. 1.Center for Machine Vision Research, Department of Computer Science and EngineeringUniversity of OuluFinland
  2. 2.Research Center for Learning ScienceSoutheast UniversityChina

Personalised recommendations