Abstract

Considering that no single algorithm available is universal in color constancy, we propose an effective combination approach using a texture-based matching strategy and a local regression with prior-knowledge regularization. To represent the images, we construct a texture pyramid using an integrated Weibull distribution. Then we define an image similarity measure to search for the K most similar images of the test image. To combine the single algorithms, we integrate prior knowledge into a regularized local regression in a decorrelated color space. Regression weights are obtained on these similar images, and the regularization is implemented by the frequency ratio of the best single algorithm. Experiments on two real world datasets show our approach outperforms the state-of-the-art single algorithms and popular combination approaches with a performance increase of at least 29% compared to the best-performing single algorithm w.r.t median angular error.

© 2010 Optical Society of America

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  17. E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
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    [CrossRef]
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    [CrossRef]
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2010 (3)

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics and scene semantics,” IEEE Trans. Pattern Anal. Mach. Intell. (2010, in press).

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recogn. 43, 695–705 (2010).
[CrossRef]

J. C. van Gemert, C. G. M. Snoek, C. J. Veenman, A. W. M. Smeulders, and J. M. Geusebroek, “Comparing Compact Codebooks for Visual Categorization,” Comput. Vis. Image Underst. 114, 450–462 (2010).
[CrossRef]

2009 (3)

A. Gijsenij, T. Gevers, and M. Lucassen, “A perceptual analysis of distance measures for color constancy algorithms,” J. Opt. Soc. Am. A 26, 2243–2256 (2009).
[CrossRef]

R. Lu, A. Gijsenij, T. Gevers, K. van de Sande, J. M. Geusebroek, and D. Xu, “Color constancy using stage classification,” in IEEE Conference on Image Processing (IEEE, 2009), pp. 685–688.

B. Li, D. Xu, and C. Y. Lang, “Colour constancy based on texture similarity for natural images,” Color. Technol. 125, 328–333 (2009).
[CrossRef]

2008 (8)

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381–2392 (2008).
[CrossRef] [PubMed]

P. Li, “An adaptive binning color model for mean shift tracking,” IEEE Trans. Circuits Syst. Video Technol. 18, 1293–1299 (2008).
[CrossRef]

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef] [PubMed]

N. Hrustemovic and M. R. Gupta, “Multiresolutional regularization of local linear regression over adaptive neighborhoods for color management,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 497–500.
[CrossRef]

H. S. Scholte, S. Ghebreab, A. Smeulders, and V. Lamme, “The parvo and magno-cellular systems encode natural image statistics parameters,” J. Vision 8, 686&686a (2008).
[CrossRef]

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.
[CrossRef]

V. Yanulevskaya, J. C. van Gemert, K. Roth, A. K. Herbol, N. Sebe, and J. M. Geusebroek, “Emotional valence categorization using holistic image features,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 101–104.
[CrossRef]

2007 (3)

J. van de Weijer, C. Schmid, and J. Verbeek, “Using high-level visual information for color constancy,” in IEEE Conference on Computer Vision (IEEE, 2007), pp. 1–8.

J. van de Weijer, C. Schmid, and J. Verbeek, “Edge-based color constancy,” IEEE Trans. Image Process. 30, 2207–2214 (2007).
[CrossRef]

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.
[CrossRef]

2006 (4)

S. D. Hordely, “Scene illluminant estimation: past, present and future,” Color Res. Appl. 31, 303–314 (2006).
[CrossRef]

J. M. Geusebroek, “Compact object descriptors from local colour invariant histograms,” in British Machine Vision Conference (British Machine Vision Association, 2006), pp. 1029–1038.

S. Lazebnik, C. Schmid, and J. Poncek, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 2169–2178.

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008–1020 (2006).
[CrossRef]

2005 (4)

F. Porikli, “Integral histogram: a fast way to extract histograms in Cartesian spaces,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 829–836.

J. M. Geusebroek and A. W. M. Smeulders, “A six-stimulus theory for stochastic texture,” Int. J. Comput. Vis. 62, 7–16 (2005).

G. Schaefer, S. Hordley, and G. Finalayson, “A combined physical and statistical approach to colour constancy,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 148–153.

J. van de Weijer and T. Gevers, “Color constancy based on the grey-edge hypothesis,” in IEEE Conference on Image Processing (IEEE, 2005), pp. 722–725.

2004 (2)

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Twelfth Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 2004), pp. 37–41.

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

2003 (1)

F. Ciurea and B. V. Funt, “A large image database for color constancy research,” in Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 2003), pp. 160–164.

2000 (1)

1999 (2)

T. Gevers and A. W. M. Smeulders, “Color-based object recognition,” Pattern Recogn. 32, 453–464 (1999).
[CrossRef]

V. C. Cardei and B. Funt, “Committee-based colour constancy,” in Seventh Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 1999), pp. 311–313.

1998 (1)

1980 (1)

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1–26 (1980).
[CrossRef]

1971 (1)

Bianco, S.

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recogn. 43, 695–705 (2010).
[CrossRef]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381–2392 (2008).
[CrossRef] [PubMed]

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

Blake, A.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.
[CrossRef]

Buchsbaum, G.

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1–26 (1980).
[CrossRef]

Cardei, V. C.

V. C. Cardei and B. Funt, “Committee-based colour constancy,” in Seventh Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 1999), pp. 311–313.

Chiao, C. C.

Ciocca, G.

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recogn. 43, 695–705 (2010).
[CrossRef]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381–2392 (2008).
[CrossRef] [PubMed]

Ciurea, F.

F. Ciurea and B. V. Funt, “A large image database for color constancy research,” in Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 2003), pp. 160–164.

Cronin, T. W.

Cusano, C.

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recogn. 43, 695–705 (2010).
[CrossRef]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381–2392 (2008).
[CrossRef] [PubMed]

Fierro, M.

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef] [PubMed]

Finalayson, G.

G. Schaefer, S. Hordley, and G. Finalayson, “A combined physical and statistical approach to colour constancy,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 148–153.

Finlayson, G. D.

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008–1020 (2006).
[CrossRef]

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Twelfth Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 2004), pp. 37–41.

Funt, B.

V. C. Cardei and B. Funt, “Committee-based colour constancy,” in Seventh Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 1999), pp. 311–313.

Funt, B. V.

F. Ciurea and B. V. Funt, “A large image database for color constancy research,” in Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 2003), pp. 160–164.

B. V. Funt and B. C. Lewis, “Diagonal versus affine transformations for color correction,” J. Opt. Soc. Am. A 17, 2108–2112 (2000).
[CrossRef]

Gasparini, F.

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

Gatta, C.

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef] [PubMed]

Gehler, P. V.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.
[CrossRef]

Geusebroek, J. M.

J. C. van Gemert, C. G. M. Snoek, C. J. Veenman, A. W. M. Smeulders, and J. M. Geusebroek, “Comparing Compact Codebooks for Visual Categorization,” Comput. Vis. Image Underst. 114, 450–462 (2010).
[CrossRef]

R. Lu, A. Gijsenij, T. Gevers, K. van de Sande, J. M. Geusebroek, and D. Xu, “Color constancy using stage classification,” in IEEE Conference on Image Processing (IEEE, 2009), pp. 685–688.

V. Yanulevskaya, J. C. van Gemert, K. Roth, A. K. Herbol, N. Sebe, and J. M. Geusebroek, “Emotional valence categorization using holistic image features,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 101–104.
[CrossRef]

J. M. Geusebroek, “Compact object descriptors from local colour invariant histograms,” in British Machine Vision Conference (British Machine Vision Association, 2006), pp. 1029–1038.

J. M. Geusebroek and A. W. M. Smeulders, “A six-stimulus theory for stochastic texture,” Int. J. Comput. Vis. 62, 7–16 (2005).

Gevers, T.

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics and scene semantics,” IEEE Trans. Pattern Anal. Mach. Intell. (2010, in press).

R. Lu, A. Gijsenij, T. Gevers, K. van de Sande, J. M. Geusebroek, and D. Xu, “Color constancy using stage classification,” in IEEE Conference on Image Processing (IEEE, 2009), pp. 685–688.

A. Gijsenij, T. Gevers, and M. Lucassen, “A perceptual analysis of distance measures for color constancy algorithms,” J. Opt. Soc. Am. A 26, 2243–2256 (2009).
[CrossRef]

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.
[CrossRef]

J. van de Weijer and T. Gevers, “Color constancy based on the grey-edge hypothesis,” in IEEE Conference on Image Processing (IEEE, 2005), pp. 722–725.

T. Gevers and A. W. M. Smeulders, “Color-based object recognition,” Pattern Recogn. 32, 453–464 (1999).
[CrossRef]

Ghebreab, S.

H. S. Scholte, S. Ghebreab, A. Smeulders, and V. Lamme, “The parvo and magno-cellular systems encode natural image statistics parameters,” J. Vision 8, 686&686a (2008).
[CrossRef]

Gijsenij, A.

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics and scene semantics,” IEEE Trans. Pattern Anal. Mach. Intell. (2010, in press).

R. Lu, A. Gijsenij, T. Gevers, K. van de Sande, J. M. Geusebroek, and D. Xu, “Color constancy using stage classification,” in IEEE Conference on Image Processing (IEEE, 2009), pp. 685–688.

A. Gijsenij, T. Gevers, and M. Lucassen, “A perceptual analysis of distance measures for color constancy algorithms,” J. Opt. Soc. Am. A 26, 2243–2256 (2009).
[CrossRef]

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.
[CrossRef]

Gupta, M. R.

N. Hrustemovic and M. R. Gupta, “Multiresolutional regularization of local linear regression over adaptive neighborhoods for color management,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 497–500.
[CrossRef]

Herbol, A. K.

V. Yanulevskaya, J. C. van Gemert, K. Roth, A. K. Herbol, N. Sebe, and J. M. Geusebroek, “Emotional valence categorization using holistic image features,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 101–104.
[CrossRef]

Hordely, S. D.

S. D. Hordely, “Scene illluminant estimation: past, present and future,” Color Res. Appl. 31, 303–314 (2006).
[CrossRef]

Hordley, S.

G. Schaefer, S. Hordley, and G. Finalayson, “A combined physical and statistical approach to colour constancy,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 148–153.

Hordley, S. D.

Hrustemovic, N.

N. Hrustemovic and M. R. Gupta, “Multiresolutional regularization of local linear regression over adaptive neighborhoods for color management,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 497–500.
[CrossRef]

Lamme, V.

H. S. Scholte, S. Ghebreab, A. Smeulders, and V. Lamme, “The parvo and magno-cellular systems encode natural image statistics parameters,” J. Vision 8, 686&686a (2008).
[CrossRef]

Land, E.

Lang, C. Y.

B. Li, D. Xu, and C. Y. Lang, “Colour constancy based on texture similarity for natural images,” Color. Technol. 125, 328–333 (2009).
[CrossRef]

Lazebnik, S.

S. Lazebnik, C. Schmid, and J. Poncek, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 2169–2178.

Lewis, B. C.

Li, B.

B. Li, D. Xu, and C. Y. Lang, “Colour constancy based on texture similarity for natural images,” Color. Technol. 125, 328–333 (2009).
[CrossRef]

Li, P.

P. Li, “An adaptive binning color model for mean shift tracking,” IEEE Trans. Circuits Syst. Video Technol. 18, 1293–1299 (2008).
[CrossRef]

Lowe, D. G.

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

Lu, R.

R. Lu, A. Gijsenij, T. Gevers, K. van de Sande, J. M. Geusebroek, and D. Xu, “Color constancy using stage classification,” in IEEE Conference on Image Processing (IEEE, 2009), pp. 685–688.

Lucassen, M.

McCann, J.

Minka, T.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.
[CrossRef]

Poncek, J.

S. Lazebnik, C. Schmid, and J. Poncek, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 2169–2178.

Porikli, F.

F. Porikli, “Integral histogram: a fast way to extract histograms in Cartesian spaces,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 829–836.

Provenzi, E.

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef] [PubMed]

Rizzi, A.

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef] [PubMed]

Roth, K.

V. Yanulevskaya, J. C. van Gemert, K. Roth, A. K. Herbol, N. Sebe, and J. M. Geusebroek, “Emotional valence categorization using holistic image features,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 101–104.
[CrossRef]

Rother, C.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.
[CrossRef]

Ruderman, D. L.

Schaefer, G.

G. Schaefer, S. Hordley, and G. Finalayson, “A combined physical and statistical approach to colour constancy,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 148–153.

Schettini, R.

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recogn. 43, 695–705 (2010).
[CrossRef]

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381–2392 (2008).
[CrossRef] [PubMed]

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

Schmid, C.

J. van de Weijer, C. Schmid, and J. Verbeek, “Using high-level visual information for color constancy,” in IEEE Conference on Computer Vision (IEEE, 2007), pp. 1–8.

J. van de Weijer, C. Schmid, and J. Verbeek, “Edge-based color constancy,” IEEE Trans. Image Process. 30, 2207–2214 (2007).
[CrossRef]

S. Lazebnik, C. Schmid, and J. Poncek, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 2169–2178.

Scholte, H. S.

H. S. Scholte, S. Ghebreab, A. Smeulders, and V. Lamme, “The parvo and magno-cellular systems encode natural image statistics parameters,” J. Vision 8, 686&686a (2008).
[CrossRef]

Sebe, N.

V. Yanulevskaya, J. C. van Gemert, K. Roth, A. K. Herbol, N. Sebe, and J. M. Geusebroek, “Emotional valence categorization using holistic image features,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 101–104.
[CrossRef]

Sharp, T.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.
[CrossRef]

Smeulders, A.

H. S. Scholte, S. Ghebreab, A. Smeulders, and V. Lamme, “The parvo and magno-cellular systems encode natural image statistics parameters,” J. Vision 8, 686&686a (2008).
[CrossRef]

Smeulders, A. W. M.

J. C. van Gemert, C. G. M. Snoek, C. J. Veenman, A. W. M. Smeulders, and J. M. Geusebroek, “Comparing Compact Codebooks for Visual Categorization,” Comput. Vis. Image Underst. 114, 450–462 (2010).
[CrossRef]

J. M. Geusebroek and A. W. M. Smeulders, “A six-stimulus theory for stochastic texture,” Int. J. Comput. Vis. 62, 7–16 (2005).

T. Gevers and A. W. M. Smeulders, “Color-based object recognition,” Pattern Recogn. 32, 453–464 (1999).
[CrossRef]

Snoek, C. G. M.

J. C. van Gemert, C. G. M. Snoek, C. J. Veenman, A. W. M. Smeulders, and J. M. Geusebroek, “Comparing Compact Codebooks for Visual Categorization,” Comput. Vis. Image Underst. 114, 450–462 (2010).
[CrossRef]

Trezzi, E.

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Twelfth Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 2004), pp. 37–41.

van de Sande, K.

R. Lu, A. Gijsenij, T. Gevers, K. van de Sande, J. M. Geusebroek, and D. Xu, “Color constancy using stage classification,” in IEEE Conference on Image Processing (IEEE, 2009), pp. 685–688.

van de Weijer, J.

J. van de Weijer, C. Schmid, and J. Verbeek, “Edge-based color constancy,” IEEE Trans. Image Process. 30, 2207–2214 (2007).
[CrossRef]

J. van de Weijer, C. Schmid, and J. Verbeek, “Using high-level visual information for color constancy,” in IEEE Conference on Computer Vision (IEEE, 2007), pp. 1–8.

J. van de Weijer and T. Gevers, “Color constancy based on the grey-edge hypothesis,” in IEEE Conference on Image Processing (IEEE, 2005), pp. 722–725.

van Gemert, J. C.

J. C. van Gemert, C. G. M. Snoek, C. J. Veenman, A. W. M. Smeulders, and J. M. Geusebroek, “Comparing Compact Codebooks for Visual Categorization,” Comput. Vis. Image Underst. 114, 450–462 (2010).
[CrossRef]

V. Yanulevskaya, J. C. van Gemert, K. Roth, A. K. Herbol, N. Sebe, and J. M. Geusebroek, “Emotional valence categorization using holistic image features,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 101–104.
[CrossRef]

Veenman, C. J.

J. C. van Gemert, C. G. M. Snoek, C. J. Veenman, A. W. M. Smeulders, and J. M. Geusebroek, “Comparing Compact Codebooks for Visual Categorization,” Comput. Vis. Image Underst. 114, 450–462 (2010).
[CrossRef]

Verbeek, J.

J. van de Weijer, C. Schmid, and J. Verbeek, “Using high-level visual information for color constancy,” in IEEE Conference on Computer Vision (IEEE, 2007), pp. 1–8.

J. van de Weijer, C. Schmid, and J. Verbeek, “Edge-based color constancy,” IEEE Trans. Image Process. 30, 2207–2214 (2007).
[CrossRef]

Xu, D.

R. Lu, A. Gijsenij, T. Gevers, K. van de Sande, J. M. Geusebroek, and D. Xu, “Color constancy using stage classification,” in IEEE Conference on Image Processing (IEEE, 2009), pp. 685–688.

B. Li, D. Xu, and C. Y. Lang, “Colour constancy based on texture similarity for natural images,” Color. Technol. 125, 328–333 (2009).
[CrossRef]

Yanulevskaya, V.

V. Yanulevskaya, J. C. van Gemert, K. Roth, A. K. Herbol, N. Sebe, and J. M. Geusebroek, “Emotional valence categorization using holistic image features,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 101–104.
[CrossRef]

Color Res. Appl. (1)

S. D. Hordely, “Scene illluminant estimation: past, present and future,” Color Res. Appl. 31, 303–314 (2006).
[CrossRef]

Color. Technol. (1)

B. Li, D. Xu, and C. Y. Lang, “Colour constancy based on texture similarity for natural images,” Color. Technol. 125, 328–333 (2009).
[CrossRef]

Comput. Vis. Image Underst. (1)

J. C. van Gemert, C. G. M. Snoek, C. J. Veenman, A. W. M. Smeulders, and J. M. Geusebroek, “Comparing Compact Codebooks for Visual Categorization,” Comput. Vis. Image Underst. 114, 450–462 (2010).
[CrossRef]

IEEE Trans. Circuits Syst. Video Technol. (1)

P. Li, “An adaptive binning color model for mean shift tracking,” IEEE Trans. Circuits Syst. Video Technol. 18, 1293–1299 (2008).
[CrossRef]

IEEE Trans. Image Process. (2)

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381–2392 (2008).
[CrossRef] [PubMed]

J. van de Weijer, C. Schmid, and J. Verbeek, “Edge-based color constancy,” IEEE Trans. Image Process. 30, 2207–2214 (2007).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef] [PubMed]

Int. J. Comput. Vis. (2)

J. M. Geusebroek and A. W. M. Smeulders, “A six-stimulus theory for stochastic texture,” Int. J. Comput. Vis. 62, 7–16 (2005).

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

J. Electron. Imaging (1)

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[CrossRef]

J. Franklin Inst. (1)

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1–26 (1980).
[CrossRef]

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (4)

J. Vision (1)

H. S. Scholte, S. Ghebreab, A. Smeulders, and V. Lamme, “The parvo and magno-cellular systems encode natural image statistics parameters,” J. Vision 8, 686&686a (2008).
[CrossRef]

Pattern Recogn. (2)

S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recogn. 43, 695–705 (2010).
[CrossRef]

T. Gevers and A. W. M. Smeulders, “Color-based object recognition,” Pattern Recogn. 32, 453–464 (1999).
[CrossRef]

Other (15)

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.
[CrossRef]

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics and scene semantics,” IEEE Trans. Pattern Anal. Mach. Intell. (2010, in press).

V. C. Cardei and B. Funt, “Committee-based colour constancy,” in Seventh Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 1999), pp. 311–313.

G. Schaefer, S. Hordley, and G. Finalayson, “A combined physical and statistical approach to colour constancy,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 148–153.

R. Lu, A. Gijsenij, T. Gevers, K. van de Sande, J. M. Geusebroek, and D. Xu, “Color constancy using stage classification,” in IEEE Conference on Image Processing (IEEE, 2009), pp. 685–688.

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Twelfth Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 2004), pp. 37–41.

J. van de Weijer and T. Gevers, “Color constancy based on the grey-edge hypothesis,” in IEEE Conference on Image Processing (IEEE, 2005), pp. 722–725.

J. van de Weijer, C. Schmid, and J. Verbeek, “Using high-level visual information for color constancy,” in IEEE Conference on Computer Vision (IEEE, 2007), pp. 1–8.

P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.
[CrossRef]

V. Yanulevskaya, J. C. van Gemert, K. Roth, A. K. Herbol, N. Sebe, and J. M. Geusebroek, “Emotional valence categorization using holistic image features,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 101–104.
[CrossRef]

J. M. Geusebroek, “Compact object descriptors from local colour invariant histograms,” in British Machine Vision Conference (British Machine Vision Association, 2006), pp. 1029–1038.

N. Hrustemovic and M. R. Gupta, “Multiresolutional regularization of local linear regression over adaptive neighborhoods for color management,” in IEEE Conference on Image Processing (IEEE, 2008), pp. 497–500.
[CrossRef]

F. Ciurea and B. V. Funt, “A large image database for color constancy research,” in Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technology and Applications (Society for Imaging Science and Technology, 2003), pp. 160–164.

S. Lazebnik, C. Schmid, and J. Poncek, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 2169–2178.

F. Porikli, “Integral histogram: a fast way to extract histograms in Cartesian spaces,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 829–836.

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Figures (4)

Fig. 1
Fig. 1

Weibull parameters distribution of x-edge for: (a) SFU Real World Dataset; (b) MS Real World Dataset. Each marker indicates the best single algorithm for the corresponding image.

Fig. 2
Fig. 2

Texture pyramid of three levels and their corresponding weights.

Fig. 3
Fig. 3

Median angular error variation with the neighborhood sizes of various schemes on: (a) SFU Real World Dataset; (b) MS Real World Dataset.

Fig. 4
Fig. 4

Corrected images on the MS Real World Dataset: (a) original image; (b) LMS correction; (c) TPM-RLR correction.

Tables (4)

Tables Icon

Table 1 Performance Evaluation for Various Methods on the SFU Real World Dataset

Tables Icon

Table 2 Performance Evaluation for Various Methods on the MS Real World Dataset

Tables Icon

Table 3 LMS Weights on the SFU Real World Dataset

Tables Icon

Table 4 LMS Weights on the MS Real World Dataset

Equations (19)

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

ρ ( x ) = ω e ( λ ) s ( x , λ ) c ( λ ) d λ ,
e = ω e ( λ ) c ( λ ) d λ .
( | n ρ σ ( x ) | p d x ) 1 p = k e n , p , σ ,
f ( x ) = γ 2 γ 1 γ θ Γ ( 1 γ ) exp { 1 γ | x μ θ | γ } ,
Γ ( x ) = 0 t x 1 exp ( t ) d t .
S l ( X , Y ) = 1 ( 2 l + 1 1 ) 2 i = 1 ( 2 l + 1 1 ) 2 min ( γ X i l , γ Y i l ) max ( γ X i l , γ Y i l ) min ( θ X i l , θ Y i l ) max ( θ X i l , θ Y i l ) ,
S image ( X , Y ) = l [ 0 , L ] w patch l S l ( X , Y ) = 1 2 L S 0 ( X , Y ) + l [ 1 , L ] 1 2 L l + 1 S l ( X , Y ) ,
( R ̂ t G ̂ t B ̂ t ) = ( R ̂ t 1 G ̂ t 1 B ̂ t 1 R ̂ t 5 G ̂ t 5 B ̂ t 5 ) w ̂ ,
( R ̂ t G ̂ t B ̂ t ) = i = 1 5 w ̂ i ( R ̂ t i G ̂ t i B ̂ t i ) ,
w ̂ j = arg min w j V T w j t j 2 + λ w j 2 ,
V = ( l ̂ i 1 1 α ̂ i 1 1 β ̂ i 1 1 l ̂ i 1 5 α ̂ i 1 5 β ̂ i 1 5 l ̂ i K 1 α ̂ i K 1 β ̂ i K 1 l ̂ i K 5 α ̂ i K 5 β ̂ i K 5 ) T
w ̂ j = arg min w j V T w j t j 2 + λ w j w ̆ j 2 .
w ̂ = ( V T V + λ I ) 1 ( V T t + λ w ̆ ) .
( w ̆ ) m , n = { K i K m = 3 i + n 3 , n [ 1 , 3 ] 0 otherwise } ,
ε = cos 1 ( e ̂ e e i ) ,
( R c G c B c ) = ( d 1 0 0 0 d 2 0 0 0 d 3 ) ( R u G u B u ) ,
( l α β ) = ( 1 3 0 0 0 1 6 0 0 0 1 2 ) ( 1 1 1 1 1 2 1 1 0 ) ( L cs M cs S cs ) ,
( L cs M cs S cs ) = ( log L cs log M cs log S cs ) ,
( L cs M cs S cs ) = ( 0.3811 0.5783 0.0402 0.1967 0.7244 0.0782 0.0241 0.1288 0.8444 ) ( R G B ) .

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