Introduction to Local Binary Patterns: New Variants and Applications

  • Sheryl Brahnam
  • Lakhmi C. Jain
  • Alessandra Lumini
  • Loris Nanni
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 506)

Abstract

This chapter provides an introduction to Local Binary Patterns (LBP) and important new variants. Some issues with LBP variants are discussed. A summary of the chapters on LBP is also presented.

References

  1. 1.
    Pietikä Pietikainen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer, London (2011)Google Scholar
  2. 2.
    Tuceryan, M., Jain, A.K.: Texture analysis. C.H. Chen, L.F. Pau, P.S.P. Wang, (eds.) The handbook of pattern recognition and computer vision, pp. 207–248: World Scientific Publishing Co., Singapore (1998)Google Scholar
  3. 3.
    Zucker, S.W.: Towards a model of texture. Comput. Graph. Image Process. 5, 190–202 (1976)CrossRefGoogle Scholar
  4. 4.
    Sklansky, J.: Image segmentation and feature extraction. IEEE Trans. Syst. Man Cybern. SMC-8, 237–247 (1978)Google Scholar
  5. 5.
    Coggins, J. M.:A framework for texture analysis based on spatial filtering, Ph.D. Thesis, Computer Science Department, Michigan State University, East Lansing, (1982)Google Scholar
  6. 6.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  7. 7.
    Tamura, H., Mori, S., Yamawaki, Y.: Textural features corresponding to visul perception, IEEE Trans. Syst. Man Cybern.SMC-8, 460–473 (1978)Google Scholar
  8. 8.
    Cross, G.R., Jain, A.K.: Markov random field texture models. IEEE Trans. Pattern Anal. Mach. Intell. 5(1), 25–39 (1983)CrossRefGoogle Scholar
  9. 9.
    Thyagarajan, K. S., Nguyen, T., Persons, C.: A maximum likelihood approach to texture classification using wavelet transform. in IEEE International Conference on Image Processing (1994)Google Scholar
  10. 10.
    Ojala, T., Pietikainen, M., Maeenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  11. 11.
    Liua, G.-H., Zhang, L., Hou, Y.-K., Li, Z-y, Yang, J.-Y.: Image retrieval based on multi-texton histogram. Pattern Recogn. 43(7), 2380–2389 (2010)CrossRefGoogle Scholar
  12. 12.
    Ahonen, T., Pietikäinen, M.: Image description using joint distribution of filter bank responses. Pattern Recogn. Lett. 30(4), 368–376 (2009)CrossRefGoogle Scholar
  13. 13.
    Unay, D., Ekin, A.: Intensity versus texture for medical image search and retrieval. In 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 241–244 ( 2008)Google Scholar
  14. 14.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. Anal. Model. Faces Gestures, LNCS 4778, 168–182 (2007)CrossRefGoogle Scholar
  15. 15.
    Keramidas, E. G., Iakovidis, D. K., Maroulis, D., Dimitropoulos, N.: Thyroid texture representation via noise resistant image features. In: Twenty-First IEEE International Symposium on Computer-Based Medical Systems (CBMS 2008), pp. 560–565 (2008)Google Scholar
  16. 16.
    Paci, M., Nanni, L., Lathi, A., Aalto-Setälä, K., Hyttinen, J., Severi, S.: Non-binary coding for texture descriptors in sub-cellular and stem cell image classification. Curr. Bioinform. 8(2) (2013)Google Scholar
  17. 17.
    Nanni, L., Lumini, A.: RegionBoost learning for 2D+3D based face recognition. Pattern Recogn. Lett. 28(15), 2063–2070 (2007)CrossRefGoogle Scholar
  18. 18.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041(2006)Google Scholar
  19. 19.
    Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)CrossRefGoogle Scholar
  20. 20.
    He, D., Wang, L.: Texture unit, texture spectrum and texture analysis. In: Geoscience and Remote Sensing, Symposium (1989)Google Scholar
  21. 21.
    He, D., Wang, L.: Texture features based on texture spectrum. Pattern Recognit. 24(5), 391–399 (1991)CrossRefGoogle Scholar
  22. 22.
    Zabih, R., Wood, J.: Non-parametric local transforms for computing visual correspondence. In: European Conference on Computer Vision (1994)Google Scholar
  23. 23.
    Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: 12th IAPR International Conference (1994)Google Scholar
  24. 24.
    Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. EE Trans. Image Process. vol. ePub (2010)Google Scholar
  25. 25.
    Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artif. Intell. Med. 49(2), 117–125 (2010)CrossRefGoogle Scholar
  26. 26.
    Akhloufi, M., and Bendada, A.: Locally adaptive texture features for multispectral face recognition. In: IEEE International Conference on Systems Man and Cybernetics (SMC), pp. 3308–314 (2010)Google Scholar
  27. 27.
    Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., Gao, W.: WLD: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2010)CrossRefGoogle Scholar
  28. 28.
    Chen, J., Zhao, G., Pietikäinen, M.: An improved local descriptor and threshold learning for unsupervised dynamic texture segmentation. In: ICCV Workshop on Machine Learning for Vision-based Motion, Analysis, pp. 460–467 (2009)Google Scholar
  29. 29.
    Ojansivu, V., and Heikkila, J.: Blur insensitive texture classification using local phase quantization. In: ICISP, pp. 236–243 (2008)Google Scholar
  30. 30.
    Lategahn, H., Gross, S., Stehle, T., Aach, T.: Texture classification by modeling joint distributions of local patterns with Gaussian mixtures. IEEE Trans. Image Process. 19, 1548–1557 (2010)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Nanni, L., Lumini, A., Brahnam, S.: Survey on LBP based texture descriptors for image classification. Expert Syst. Appl. 39(3), 3634–3641 (2012)CrossRefGoogle Scholar
  32. 32.
    Nanni, L., Paci, M., Brahnam, S., Ghidoni, S., Menegatti, E.: Virus image classification using different texture descriptors, in The 14th International Conference on Bioinformatics and Computational Biology (BIOCOMP’13). Las Vegas, NV (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sheryl Brahnam
    • 1
  • Lakhmi C. Jain
    • 2
  • Alessandra Lumini
    • 3
  • Loris Nanni
    • 4
  1. 1.Computer Information SystemsMissouri State UniversitySpringfieldUSA
  2. 2.University of CanberraACTAustralia
  3. 3.Department of Computer Science and Engineering (DISI)Università di BolognaCesenaItaly
  4. 4.Departimento di Elettronica e Informatica (DEI)Università di PadovaPaduaItaly

Personalised recommendations