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

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  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.

2013 (3)

C. Cusano, P. Napoletano, and R. Schettini, “Intensity and color descriptors for texture classification,” Proc. SPIE 8661, 866113 (2013).

R. Khan, D. Muselet, and A. Trémeau, “Texture classification across illumination color variations,” Int. J. Comput. Theory Eng. 5, 65–70 (2013).
[CrossRef]

S. Hossain and S. Serikawa, “Texture databases—a comprehensive survey,” Pattern Recogn. Lett. 34, 2007–2022 (2013).
[CrossRef]

2012 (2)

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]

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]

2011 (2)

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]

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]

2010 (2)

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]

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]

2009 (1)

A. Alsam and M. Drew, “Fast multispectral2gray,” J. Imaging Sci. Technol. 53, 60401 (2009).
[CrossRef]

2007 (1)

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

2005 (1)

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]

2004 (2)

B. Funt, F. Ciurea, and J. McCann, “Retinex in MATLAB,” J. Electron. Imaging 13, 48–57 (2004).
[CrossRef]

T. Mäenpää and M. Pietikäinen, “Classification with color and texture: jointly or separately?” Pattern Recogn. 37, 1629–1640 (2004).
[CrossRef]

2002 (1)

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]

2000 (1)

M. Luo, “A review of chromatic adaptation transforms,” Rev. Progr. Coloration Rel. Top. 30, 77–92 (2000).
[CrossRef]

1996 (1)

A. Poirson and B. Wandell, “Pattern-color separable pathways predict sensitivity to simple colored patterns,” Vis. Res. 36, 515–526 (1996).
[CrossRef]

1986 (1)

E. Land, “Recent advances in retinex theory,” Vis. Res. 26, 7–21 (1986).
[CrossRef]

1971 (1)

Adjeroh, D.

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]

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]

Ahonen, T.

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.

Ali, S.

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.

Alsam, A.

A. Alsam and M. Drew, “Fast multispectral2gray,” J. Imaging Sci. Technol. 53, 60401 (2009).
[CrossRef]

Bianco, S.

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]

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.

Bianconi, F.

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]

Bloj, M.

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.

Chan, C.-H.

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.

Ciurea, F.

B. Funt, F. Ciurea, and J. McCann, “Retinex in MATLAB,” J. Electron. Imaging 13, 48–57 (2004).
[CrossRef]

Connah, D.

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.

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.

Crowley, J.

G. Finlayson, B. Schiele, and J. Crowley, “Comprehensive colour image normalization,” in European Conference on Computer Vision (Springer, 1998), pp. 475–490.

Cusano, C.

C. Cusano, P. Napoletano, and R. Schettini, “Intensity and color descriptors for texture classification,” Proc. SPIE 8661, 866113 (2013).

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.

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.

Drbohlav, O.

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]

Drew, M.

A. Alsam and M. Drew, “Fast multispectral2gray,” J. Imaging Sci. Technol. 53, 60401 (2009).
[CrossRef]

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.

Fairchild, M.

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.

Felsberg, M.

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.

Fernández, A.

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]

Finlayson, G.

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]

G. Finlayson, B. Schiele, and J. Crowley, “Comprehensive colour image normalization,” in European Conference on Computer Vision (Springer, 1998), pp. 475–490.

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.

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.

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.

Frankle, J.

J. Frankle and J. McCann, “Method and apparatus for lightness imaging,” U.S. patent4,384,336 (May 17, 1983).

Funt, B.

B. Funt, F. Ciurea, and J. McCann, “Retinex in MATLAB,” J. Electron. Imaging 13, 48–57 (2004).
[CrossRef]

Gevers, T.

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]

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

Gijsenij, A.

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]

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

Hadid, A.

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.

Hanbury, A.

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]

Harvey, R.

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]

Hordley, S.

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]

Hossain, S.

S. Hossain and S. Serikawa, “Texture databases—a comprehensive survey,” Pattern Recogn. Lett. 34, 2007–2022 (2013).
[CrossRef]

Hunt, R.

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.

Huovinen, S.

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.

Kandaswamy, U.

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]

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]

Khan, F.

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.

Khan, R.

R. Khan, D. Muselet, and A. Trémeau, “Texture classification across illumination color variations,” Int. J. Comput. Theory Eng. 5, 65–70 (2013).
[CrossRef]

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.

Kittler, J.

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.

Kyllönen, J.

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.

Land, E.

E. Land, “Recent advances in retinex theory,” Vis. Res. 26, 7–21 (1986).
[CrossRef]

E. Land and J. McCann, “Lightness and retinex theory,” J. Opt. Soc. Am. 61, 1–11 (1971).
[CrossRef]

Leonardis, A.

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]

Li, C.

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.

Luo, M.

M. Luo, “A review of chromatic adaptation transforms,” Rev. Progr. Coloration Rel. Top. 30, 77–92 (2000).
[CrossRef]

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.

Mäenpää, T.

T. Mäenpää and M. Pietikäinen, “Classification with color and texture: jointly or separately?” Pattern Recogn. 37, 1629–1640 (2004).
[CrossRef]

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.

Mänepää, T.

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]

McCann, J.

B. Funt, F. Ciurea, and J. McCann, “Retinex in MATLAB,” J. Electron. Imaging 13, 48–57 (2004).
[CrossRef]

E. Land and J. McCann, “Lightness and retinex theory,” J. Opt. Soc. Am. 61, 1–11 (1971).
[CrossRef]

J. Frankle and J. McCann, “Method and apparatus for lightness imaging,” U.S. patent4,384,336 (May 17, 1983).

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.

Messer, K.

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.

Moroney, N.

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.

Muselet, D.

R. Khan, D. Muselet, and A. Trémeau, “Texture classification across illumination color variations,” Int. J. Comput. Theory Eng. 5, 65–70 (2013).
[CrossRef]

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.

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.

Napoletano, P.

C. Cusano, P. Napoletano, and R. Schettini, “Intensity and color descriptors for texture classification,” Proc. SPIE 8661, 866113 (2013).

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.

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.

Newman, T.

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.

Ojala, T.

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]

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.

Pietikäinen, M.

T. Mäenpää and M. Pietikäinen, “Classification with color and texture: jointly or separately?” Pattern Recogn. 37, 1629–1640 (2004).
[CrossRef]

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]

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.

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.

Poirson, A.

A. Poirson and B. Wandell, “Pattern-color separable pathways predict sensitivity to simple colored patterns,” Vis. Res. 36, 515–526 (1996).
[CrossRef]

Schaefer, G.

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]

Schettini, R.

C. Cusano, P. Napoletano, and R. Schettini, “Intensity and color descriptors for texture classification,” Proc. SPIE 8661, 866113 (2013).

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]

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.

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.

Schiele, B.

G. Finlayson, B. Schiele, and J. Crowley, “Comprehensive colour image normalization,” in European Conference on Computer Vision (Springer, 1998), pp. 475–490.

Schuckers, S.

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]

Schuckers, S. A.

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]

Seifi, M.

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.

Serikawa, S.

S. Hossain and S. Serikawa, “Texture databases—a comprehensive survey,” Pattern Recogn. Lett. 34, 2007–2022 (2013).
[CrossRef]

Song, X.

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.

Southam, P.

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]

Tremeau, A.

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.

Trémeau, A.

R. Khan, D. Muselet, and A. Trémeau, “Texture classification across illumination color variations,” Int. J. Comput. Theory Eng. 5, 65–70 (2013).
[CrossRef]

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.

Trezzi, E.

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.

van de Weijer, J.

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]

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

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.

Viertola, J.

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.

von Kries, J.

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.

Wandell, B.

A. Poirson and B. Wandell, “Pattern-color separable pathways predict sensitivity to simple colored patterns,” Vis. Res. 36, 515–526 (1996).
[CrossRef]

Yun Tian, G.

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]

Zhao, G.

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.

Color Res. Appl. (1)

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]

Comput. Vis. Image Underst. (1)

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]

IEEE Trans. Image Process. (2)

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]

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

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]

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]

IEEE Trans. Syst. Man Cyber. Part B (1)

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]

Int. J. Comput. Theory Eng. (1)

R. Khan, D. Muselet, and A. Trémeau, “Texture classification across illumination color variations,” Int. J. Comput. Theory Eng. 5, 65–70 (2013).
[CrossRef]

J. Electron. Imaging (2)

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]

B. Funt, F. Ciurea, and J. McCann, “Retinex in MATLAB,” J. Electron. Imaging 13, 48–57 (2004).
[CrossRef]

J. Imaging Sci. Technol. (1)

A. Alsam and M. Drew, “Fast multispectral2gray,” J. Imaging Sci. Technol. 53, 60401 (2009).
[CrossRef]

J. Opt. Soc. Am. (1)

Pattern Recogn. (2)

T. Mäenpää and M. Pietikäinen, “Classification with color and texture: jointly or separately?” Pattern Recogn. 37, 1629–1640 (2004).
[CrossRef]

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]

Pattern Recogn. Lett. (1)

S. Hossain and S. Serikawa, “Texture databases—a comprehensive survey,” Pattern Recogn. Lett. 34, 2007–2022 (2013).
[CrossRef]

Proc. SPIE (1)

C. Cusano, P. Napoletano, and R. Schettini, “Intensity and color descriptors for texture classification,” Proc. SPIE 8661, 866113 (2013).

Rev. Progr. Coloration Rel. Top. (1)

M. Luo, “A review of chromatic adaptation transforms,” Rev. Progr. Coloration Rel. Top. 30, 77–92 (2000).
[CrossRef]

Vis. Res. (2)

A. Poirson and B. Wandell, “Pattern-color separable pathways predict sensitivity to simple colored patterns,” Vis. Res. 36, 515–526 (1996).
[CrossRef]

E. Land, “Recent advances in retinex theory,” Vis. Res. 26, 7–21 (1986).
[CrossRef]

Other (16)

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.

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.

J. Frankle and J. McCann, “Method and apparatus for lightness imaging,” U.S. patent4,384,336 (May 17, 1983).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

G. Finlayson, B. Schiele, and J. Crowley, “Comprehensive colour image normalization,” in European Conference on Computer Vision (Springer, 1998), pp. 475–490.

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.

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (5)

Fig. 1.
Fig. 1.

Diagram representing the computation of the LCC corresponding to the neighborhood defined by the parameters n=16 and r=2. The color vector c¯ is computed by averaging the 16 neighbors. Then, the local contrast is measured by the angle θ between the average c¯ and the central color vector c^.

Fig. 2.
Fig. 2.

(b)–(d) LCC maps and (e)–(f) LCC distributions obtained from a sample image (a). The neighborhoods considered are, from left to right, n=8 and r=1, n=16 and r=2, and n=24 and r=3. In the maps, brighter pixels stand for higher values of LCC.

Fig. 3.
Fig. 3.

Examples of images in six of the 68 classes. Each column contains images from a different class. The first row contains images under “inca”, the second row contains images under “TL84”, and the last row contains images under “horizon” (best viewed in color).

Fig. 4.
Fig. 4.

Performance of the combination of LBP16,2u and LCC obtained by varying (a) combination weight w and (b) number of bins Q of the LCC histogram. In the first plot Q was fixed to 256, and in the second w was fixed to 0.15.

Fig. 5.
Fig. 5.

Average classification rates obtained in the case of variable illumination. The bars indicate the minimum and maximum performance obtained on the six combinations of training and test illuminants.

Tables (5)

Tables Icon

Table 1. Evaluation of Several Variants of LBPsa

Tables Icon

Table 2. Evaluation of Several Variants of LBPs Combined with the LCC Descriptora

Tables Icon

Table 3. Average Accuracy of Several Variants of LBPs Combined with Different Preprocessing Methods Experimented on Subsets without Illuminant Variationsa

Tables Icon

Table 4. Average Accuracy of Several Variants of LBPs Combined with Different Preprocessing Methods Experimented on Subsets with Illuminant Variationsa

Tables Icon

Table 5. Comparison of Best LBPs with Best LBPs+LCC and Other Methods in the State of the Arta

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

LBPn,r(g^)=p=0n1s(gpg^)2p,
θ=arccos(c^,c¯c^·c¯),
c¯=1np=0n1cp,
c¯=1(2W+1)2i=WWj=WWcijcij,
D=(HLBPw×HLCC).
χ2(x,y)=12i=1N(xiyi)2xi+yi,

Metrics