LBP-Motivated Colour Texture Classification

  • Raquel Bello-CerezoEmail author
  • Paul Fieguth
  • Francesco Bianconi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)


In this paper we investigate extensions of Local Binary Patterns (LBP), Improved Local Binary Patterns (ILBP) and Extended Local Binary Patterns (ELBP) to colour textures via two different strategies: intra-/inter-channel features and colour orderings. We experimentally evaluate the proposed methods over 15 datasets of general and biomedical colour textures. Intra- and inter-channel features from the RGB space emerged as the best descriptors and we found that the best accuracy was achieved by combining multi-resolution intra-channel features with single-resolution inter-channel features.


Colour Texture Local Binary Patterns 



R. Bello-Cerezo wants to thank the colleagues at Systems Design Engineering, University of Waterloo, Canada, for the assistance received during her research visit from Sep. 2017 to Feb. 2018. F. Bianconi wishes to acknowledge support from the Italian Ministry of University and Research (MIUR) under the Individual Funding Scheme for Fundamental Research (‘FFABR’ 2017) and from the Department of Engineering at the Università degli Studi di Perugia, Italy, under the Fundamental Research Grants Scheme 2018.


  1. 1.
    Weszka, J.S., Rosenfeld, A.: An application of texture analysis to materials inspection. Pattern Recognit. 8(4), 195–200 (1976)Google Scholar
  2. 2.
    Tsai, D.M., Huang, T.Y.: Automated surface inspection for statistical textures. Image Vis. Comput. 21(4), 307–323 (2003)Google Scholar
  3. 3.
    Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., Fieguth, P.: A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv. Eng. Inform. 29(2), 196–210 (2015)Google Scholar
  4. 4.
    Meijer, G.A., Beliën, J.A.M., Van Diest, P.J., Baak, J.P.A.: Image analysis in clinical pathology. J. Clin. Pathol. 50(5), 365–370 (1997)Google Scholar
  5. 5.
    Linder, N., et al.: Identification of tumor epithelium and stroma in tissue microarrays using texture analysis. Diagn. Pathol. 7(22), 1–11 (2012)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    Jalalian, A., Mashohor, S., Mahmud, R., Karasfi, B., Saripan, I., Ramli, A.R.: Computer-assisted diagnosis system for breast cancer in computed tomography laser mammography (CTLM). J. Digit. Imaging 30(6), 796–811 (2017)Google Scholar
  8. 8.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of Seventh IEEE International Conference on Computer Vision, 1999, vol. 2, pp. 1150–1157 (1999)Google Scholar
  9. 9.
    Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 411–426 (2007)Google Scholar
  10. 10.
    Liu, H., Wu, Y., Sun, F., Guo, D.: Recent progress on tactile object recognition. Int. J. Adv. Robot. Syst. 14(4) (2017)Google Scholar
  11. 11.
    Drimbarean, A., Whelan, P.: Experiments in colour texture analysis. Pattern Recognit. Lett. 22(10), 1161–1167 (2001)zbMATHGoogle Scholar
  12. 12.
    Mäenpää, T., Pietikäinen, M.: Classification with color and texture: jointly or separately? Pattern Recognit. Lett. 37(8), 1629–1640 (2004)Google Scholar
  13. 13.
    Cavina-Pratesi, C., Kentridge, R.W., Heywood, C., Milner, A.: Separate channels for processing form, texture, and color: evidence from FMRI adaptation and visual object agnosia. Cereb. Cortex 20(10), 2319–32 (2010)Google Scholar
  14. 14.
    Palm, C.: Color texture classification by integrative co-occurrence matrices. Pattern Recognit. 37(5), 965–976 (2004)Google Scholar
  15. 15.
    Bianconi, F., Harvey, R., Southam, P., Fernández, A.: Theoretical and experimental comparison of different approaches for color texture classification. J. Electron. Imaging 20(4) (2011). Article number 043006MathSciNetGoogle Scholar
  16. 16.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)zbMATHGoogle Scholar
  17. 17.
    Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C 41(6), 765–781 (2017)Google Scholar
  18. 18.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Computational Imaging and Vision, vol. 40. Springer, Heidelberg (2011). Scholar
  19. 19.
    Brahnam, S., Jain, L., Nanni, L., Lumini, A.: Local Binary Patterns: New Variants and Applications. Studies in Computational Intelligence, vol. 506. Springer, Heidelberg (2014). Scholar
  20. 20.
    Pietikäinen, M., Zhao, G.: Two decades of local binary patterns: a survey. In: Bingham, E., Kaski, S., Laaksonen, J., Lampinen, J. (eds.) Advances in Independent Component Analysis and Learning Machines, pp. 175–210. Academic Press, London (2015)Google Scholar
  21. 21.
    Liu, L., Lao, S., Fieguth, P., Guo, Y., Wang, X., Pietikäinen, M.: Median robust extended local binary pattern for texture classification. IEEE Trans. Image Process. 25(3), 1368–1381 (2016)MathSciNetGoogle Scholar
  22. 22.
    Liu, L., Fieguth, P., Guo, Y., Wang, X., Pietikäinen, M.: Local binary features for texture classification: taxonomy and experimental study. Pattern Recognit. 62, 135–160 (2017)Google Scholar
  23. 23.
    Fernández, A., Álvarez, M.X., Bianconi, F.: Texture description through histograms of equivalent patterns. J. Math. Imaging Vis. 45(1), 76–102 (2013)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Jin, H., Liu, Q., Lu, H., Tong, X.: Face detection using improved LBP under Bayesian framework. In: Proceedings of the 3rd International Conference on Image and Graphics, Hong Kong, China, pp. 306–309, December 2004Google Scholar
  25. 25.
    Liu, L., Zhao, L., Long, Y., Kuang, G., Fieguth, P.: Extended local binary patterns for texture classification. Image Vis. Comput. 30(2), 86–99 (2012)Google Scholar
  26. 26.
    Bianconi, F., Fernández, A.: A unifying framework for LBP and related methods. In: Brahnam, S., Jain, L.C., Nanni, L., Lumini, A. (eds.) Local Binary Patterns: New Variants and Applications. Studies in Computational Intelligence, vol. 506, pp. 17–46. Springer, Heidelberg (2014). Scholar
  27. 27.
    Charalambides, C.A.: Enumerative Combinatorics. Discrete Mathematics and Its Applications. Chapman and Hall/CRC, Boca Raton (2002)zbMATHGoogle Scholar
  28. 28.
    Jain, A., Healey, G.: A multiscale representation including opponent color features for texture recognition. IEEE Trans. Image Process. 7(1), 124–128 (1998)Google Scholar
  29. 29.
    Mäenpää, T., Pietikäinen, M.: Texture analysis with local binary patterns. In: Chen, C.H., Wang, P.S.P. (eds.) Handbook of Pattern Recognition and Computer Vision, 3rd edn, pp. 197–216. World Scientific Publishing, London (2005)Google Scholar
  30. 30.
    Bianconi, F., Bello-Cerezo, R., Napoletano, P.: Improved opponent colour local binary patterns: an effective local image descriptor for colour texture classification. J. Electron. Imaging 27(1) (2017)Google Scholar
  31. 31.
    Barnett, V.: The ordering of multivariate data. J. R. Stat. Soc. Ser. A (Gen.) 139(3), 318–355 (1976)MathSciNetGoogle Scholar
  32. 32.
    Aptoula, E., Lefèvre, S.: A comparative study ion multivariate mathematical morphology. Pattern Recognit. 40(11), 2914–2929 (2007)zbMATHGoogle Scholar
  33. 33.
    Porebski, A., Vandenbroucke, N., Macaire, L.: Haralick feature extraction from LBP images for colour texture classification. In: Proceedings of the International Workshops on Image Processing Theory, Tools and Applications (IPTA 2008), Sousse, Tunisie, pp. 1–8 (2008)Google Scholar
  34. 34.
    Barra, V.: Expanding the local binary pattern to multispectral images using total orderings. In: Richard, P., Braz, J. (eds.) VISIGRAPP 2010. CCIS, vol. 229, pp. 67–80. Springer, Heidelberg (2011). Scholar
  35. 35.
    Ledoux, A., Richard, N., Capelle-Laizé, A.-S., Fernandez-Maloigne, C.: Toward a complete inclusion of the vector information in morphological computation of texture features for color images. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2014. LNCS, vol. 8509, pp. 222–229. Springer, Cham (2014). Scholar
  36. 36.
    Ledoux, A., Losson, O., Macaire, L.: Color local binary patterns: compact descriptors for texture classification. J. Electron. Imaging 25(6) (2016)Google Scholar
  37. 37.
    Fernández, A., Lima, D., Bianconi, F., Smeraldi, F.: Compact colour texture descriptor based on rank transform and product ordering in the RGB color space. In: Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) (2017)Google Scholar
  38. 38.
    Palus, H.: Representations of colour images in different colour spaces. In: Sangwine, S.J., Horne, R.E.N. (eds.) The Colour Image Processing Handbook, pp. 67–90. Springer, Boston (1998). Scholar
  39. 39.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016)Google Scholar
  40. 40.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 5th International Conference on Learning Representations, San Diego, USA, May 2015Google Scholar
  41. 41.
    Cimpoi, M., Maji, S., Kokkinos, I., Vedaldi, A.: Deep filter banks for texture recognition, description, and segmentation. Int. J. Comput. Vis. 118(1), 65–94 (2016)MathSciNetGoogle Scholar
  42. 42.
    Cusano, C., Napoletano, P., Schettini, R.: Evaluating color texture descriptors under large variations of controlled lighting conditions. J. Opt. Soc. Am. A 33(1), 17–30 (2016)Google Scholar
  43. 43.
    Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the significance of real-world conditions for material classification. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004). Scholar
  44. 44.
    The kth-tips and kth-tips2 image databases. Accessed 11 Jan 2017
  45. 45.
    Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV 2005), vol. 2, pp. 1597–1604 (2005)Google Scholar
  46. 46.
    Outex texture database. Accessed 12 Jan 2017
  47. 47.
    Casanova, D., Sá, J.J., Bruno, O.: Plant leaf identification using Gabor wavelets. Int. J. Imaging Syst. Technol. 19(3), 236–246 (2009)Google Scholar
  48. 48.
  49. 49.
    Martins, J., Oliveira, L.S., Nigkoski, S., Sabourin, R.: A database for automatic classification of forest species. Mach. Vis. Appl. 24(3), 567–578 (2013)Google Scholar
  50. 50.
    New BarkTex benchmark image test suite for evaluating color texture classification schemes. Accessed 12 Jan 2017
  51. 51.
    Porebski, A., Vandenbroucke, N., Macaire, L., Hamad, D.: A new benchmark image test suite for evaluating color texture classification schemes. Multimed. Tools Appl. J. 70(1), 543–556 (2014)Google Scholar
  52. 52.
    CUReT: columbia-utrecht reflectance and texture database. Accessed 25 Jan 2017
  53. 53.
    Visual geometry group: CUReT: columbia-utrecht reflectance and texture database. Accessed 26 Jan 2017
  54. 54.
    BioMediTechRPE database (2016). Accessed 16 May 2017
  55. 55.
    Nanni, L., Paci, M., Santos, F.L.C., Skottman, H., Juuti-Uusitalo, K., Hyttinen, J.: Texture descriptors ensembles enable image-based classification of maturation of human stem cell-derived retinal pigmented epithelium. Plos One 11(2) (2016)Google Scholar
  56. 56.
    Breast cancer histopathological database (breakhis) (2015). Accessed 16 May 2017
  57. 57.
    Spanhol, F., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: International Joint Conference on Neural Networks (IJCNN 2016), Vancouver, Canada (2016)Google Scholar
  58. 58.
    Webmicroscope. EGFR colon TMA stroma LBP classification (2012). Accessed 16 May 2017
  59. 59.
    Collection of texture in colorectal cancer histology (2016). Accessed 16 May 2017
  60. 60.
    Kather, J.N., Marx, A., Reyes-Aldasoro, C.C., Schad, L.R., Zöllner, F.G., Weis, C.A.: Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-side images. Oncotarget 6(22), 19163–19176 (2015)Google Scholar
  61. 61.
    Kather, J.N., et al.: Multi-class texture analysis in colorectal cancer histology. Sci. Rep. 6 (2016). 27988Google Scholar
  62. 62.
    Bello-Cerezo, R., Bianconi, F., Cascianelli, S., Fravolini, M.L., di Maria, F., Smeraldi, F.: Hand-designed local image descriptors vs. off-the-shelf CNN-based features for texture classification: an experimental comparison. In: De Pietro, G., Gallo, L., Howlett, R.J., Jain, L.C. (eds.) KES-IIMSS 2017. SIST, vol. 76, pp. 1–10. Springer, Cham (2018). Scholar
  63. 63.
    Orjuela, S., Quinones, R., Ortiz-Jaramillo, B., Rooms, F., De Keyser, R., Philips, W.: Improving textures discrimination in the local binary patterns technique by using symmetry & group theory. In: Proceedings of the 17th International Conference on Digital Signal Processing, Corfu, Greece, July 2011. Article no. 6004978Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Raquel Bello-Cerezo
    • 1
    Email author
  • Paul Fieguth
    • 2
  • Francesco Bianconi
    • 1
  1. 1.Department of EngineeringUniversità degli Studi di PerugiaPerugiaItaly
  2. 2.Systems Design EngineeringUniversity of WaterlooWaterlooCanada

Personalised recommendations