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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Franco Gori
  • Vol. 31, Iss. 7 — Jul. 1, 2014
  • pp: 1453–1461

Combining local binary patterns and local color contrast for texture classification under varying illumination

Claudio Cusano, Paolo Napoletano, and Raimondo Schettini  »View Author Affiliations


JOSA A, Vol. 31, Issue 7, pp. 1453-1461 (2014)
http://dx.doi.org/10.1364/JOSAA.31.001453


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Abstract

This paper presents a texture descriptor for color texture classification specially designed to be robust against changes in the illumination conditions. The descriptor combines a histogram of local binary patterns (LBPs) with a novel feature measuring the distribution of local color contrast. The proposed descriptor is invariant with respect to rotations and translations of the image plane and with respect to several transformations in the color space. We evaluated the proposed descriptor on the Outex test suite, by measuring the classification accuracy in the case in which training and test images have been acquired under different illuminants. The results obtained show that our descriptor outperforms the original LBP approach and its color variants, even when these are computed after color normalization. Moreover, it also outperforms several other color texture descriptors in the state of the art.

© 2014 Optical Society of America

OCIS Codes
(100.2960) Image processing : Image analysis
(100.5010) Image processing : Pattern recognition
(330.1690) Vision, color, and visual optics : Color
(100.3008) Image processing : Image recognition, algorithms and filters

ToC Category:
Image Processing

History
Original Manuscript: November 14, 2013
Revised Manuscript: March 20, 2014
Manuscript Accepted: April 21, 2014
Published: June 12, 2014

Virtual Issues
Vol. 9, Iss. 9 Virtual Journal for Biomedical Optics

Citation
Claudio Cusano, Paolo Napoletano, and Raimondo Schettini, "Combining local binary patterns and local color contrast for texture classification under varying illumination," J. Opt. Soc. Am. A 31, 1453-1461 (2014)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-31-7-1453


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References

  1. T. Mäenpää and M. Pietikäinen, “Classification with color and texture: jointly or separately?” Pattern Recogn. 37, 1629–1640 (2004). [CrossRef]
  2. A. Poirson and B. Wandell, “Pattern-color separable pathways predict sensitivity to simple colored patterns,” Vis. Res. 36, 515–526 (1996). [CrossRef]
  3. F. Bianconi, R. Harvey, P. Southam, and A. Fernández, “Theoretical and experimental comparison of different approaches for color texture classification,” J. Electron. Imaging 20, 043006 (2011). [CrossRef]
  4. O. Drbohlav and A. Leonardis, “Towards correct and informative evaluation methodology for texture classification under varying viewpoint and illumination,” Comput. Vis. Image Underst. 114, 439–449 (2010). [CrossRef]
  5. U. Kandaswamy, S. A. Schuckers, and D. Adjeroh, “Comparison of texture analysis schemes under nonideal conditions,” IEEE Trans. Image Process. 20, 2260–2275 (2011). [CrossRef]
  6. G. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Color and Imaging Conference (Society for Imaging Science and Technology, 2004), pp. 37–41.
  7. E. Land and J. McCann, “Lightness and retinex theory,” J. Opt. Soc. Am. 61, 1–11 (1971). [CrossRef]
  8. J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007). [CrossRef]
  9. A. Gijsenij, T. Gevers, and J. van de Weijer, “Improving color constancy by photometric edge weighting,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 918–929 (2012). [CrossRef]
  10. G. Finlayson, B. Schiele, and J. Crowley, “Comprehensive colour image normalization,” in European Conference on Computer Vision (Springer, 1998), pp. 475–490.
  11. R. Khan, D. Muselet, and A. Trémeau, “Classical texture features and illumination color variations,” in Proceedings of Third International Conference on Machine Vision (IEEE, 2010), pp. 280–285.
  12. G. Finlayson, S. Hordley, G. Schaefer, and G. Yun Tian, “Illuminant and device invariant colour using histogram equalisation,” Pattern Recogn. 38, 179–190 (2005). [CrossRef]
  13. M. Seifi, X. Song, D. Muselet, and A. Tremeau, “Color texture classification across illumination changes,” in Conference on Colour in Graphics, Imaging, and Vision (Society for Imaging Science and Technology, 2010), pp. 332–337.
  14. T. Ojala, M. Pietikäinen, and T. Mänepää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002). [CrossRef]
  15. C. Cusano, P. Napoletano, and R. Schettini, “Illuminant invariant descriptors for color texture classification,” in Computational Color Imaging, Vol. 7786 of Lecture Notes in Computer Science (Springer, 2013), pp. 239–249.
  16. C. Cusano, P. Napoletano, and R. Schettini, “Intensity and color descriptors for texture classification,” Proc. SPIE 8661, 866113 (2013).
  17. C.-H. Chan, J. Kittler, and K. Messer, “Multispectral local binary pattern histogram for component-based color face verification,” in First IEEE International Conference on Biometrics: Theory, Applications, and Systems (IEEE, 2007), pp. 1–7.
  18. D. Connah and G. Finlayson, “Using local binary pattern operators for colour constant image indexing,” in Proceedings of European Conference on Color in Graphics, Imaging, and Vision (Society for Imaging Science and Technology, 2006), pp. 60–64.
  19. U. Kandaswamy, D. Adjeroh, S. Schuckers, and A. Hanbury, “Robust color texture features under varying illumination conditions,” IEEE Trans. Syst. Man Cyber. Part B 42, 58–68 (2012). [CrossRef]
  20. R. Khan, D. Muselet, and A. Trémeau, “Texture classification across illumination color variations,” Int. J. Comput. Theory Eng. 5, 65–70 (2013). [CrossRef]
  21. T. Ojala, T. Mäenpää, M. Pietikäinen, J. Viertola, J. Kyllönen, and S. Huovinen, “Outex-new framework for empirical evaluation of texture analysis algorithms,” in 16th International Conference on Pattern Recognition, Vol. 1 (IEEE, 2002), pp. 701–706.
  22. M. Pietikäinen, A. Hadid, G. Zhao, and T. Ahonen, “Local binary patterns for still images,” in Computer Vision Using Local Binary Patterns, Vol. 40 of Computational Imaging and Vision (Springer, 2011), pp. 13–47.
  23. M. Drew, D. Connah, G. Finlayson, and M. Bloj, “Improved colour to greyscale via integrability correction,” in IS&T/SPIE Electronic Imaging (International Society for Optics and Photonics, 2009), p. 72401B.
  24. A. Alsam and M. Drew, “Fast multispectral2gray,” J. Imaging Sci. Technol. 53, 60401 (2009). [CrossRef]
  25. F. Khan, J. van de Weijer, S. Ali, and M. Felsberg, “Evaluating the impact of color on texture recognition,” in Proceedings of International Conference on Computer Analysis of Images and Patterns (Springer, 2013), pp. 154–162.
  26. J. McCann, “Lessons learned from mondrians applied to real images and color gamuts,” in Color and Imaging Conference (Society for Imaging Science and Technology, 1999), pp. 1–8.
  27. B. Funt, F. Ciurea, and J. McCann, “Retinex in MATLAB,” J. Electron. Imaging 13, 48–57 (2004). [CrossRef]
  28. J. Frankle and J. McCann, “Method and apparatus for lightness imaging,” U.S. patent4,384,336 (May 17, 1983).
  29. E. Land, “Recent advances in retinex theory,” Vis. Res. 26, 7–21 (1986). [CrossRef]
  30. J. von Kries, “Chromatic adaptation,” [originally published in Festschrift der Albrecht-Ludwigs-Universität (Fribourg, Germany, 1902), pp. 145–148], in Sources of Color Vision, L. D. MacAdam, ed. (MIT, 1970), pp. 109–126.
  31. N. Moroney, M. Fairchild, R. Hunt, C. Li, M. Luo, and T. Newman, “The CIECAM02 color appearance model,” in Color and Imaging Conference (Society for Imaging Science and Technology, 2002), pp. 23–27.
  32. M. Luo, “A review of chromatic adaptation transforms,” Rev. Progr. Coloration Rel. Top. 30, 77–92 (2000). [CrossRef]
  33. S. Bianco and R. Schettini, “Two new Von Kries based chromatic adaptation transforms found by numerical optimization,” Color Res. Appl. 35, 184–192 (2010). [CrossRef]
  34. S. Hossain and S. Serikawa, “Texture databases—a comprehensive survey,” Pattern Recogn. Lett. 34, 2007–2022 (2013). [CrossRef]
  35. S. Bianco, C. Cusano, P. Napoletano, and R. Schettini, “On the robustness of color texture descriptors across illuminants,” in 17th International Conference on Image Analysis and Applications (ICIAP), Vol. 8157 of Lecture Notes in Computer Science (Springer, 2013), pp. 652–662.

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