This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Color Constancy Using Natural Image Statistics and Scene Semantics
April 2011 (vol. 33 no. 4)
pp. 687-698
ASCII Text
x 
 
Arjan Gijsenij, Theo Gevers, "Color Constancy Using Natural Image Statistics and Scene Semantics," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 4, pp. 687-698, April, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2010.93,
author = {Arjan Gijsenij and Theo Gevers},
title = {Color Constancy Using Natural Image Statistics and Scene Semantics},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {33},
number = {4},
issn = {0162-8828},
year = {2011},
pages = {687-698},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.93},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Color Constancy Using Natural Image Statistics and Scene Semantics
IS - 4
SN - 0162-8828
SP687
EP698
EPD - 687-698
A1 - Arjan Gijsenij,
A1 - Theo Gevers,
PY - 2011
KW - Color constancy
KW - illuminant estimation
KW - natural image statistics
KW - scene semantics
KW - computer vision.
VL - 33
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Arjan Gijsenij, University of Amsterdam, Amsterdam
Theo Gevers, Faculty of Science, University of Amsterdam, Amsterdam
Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of color constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing color constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in color constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific color constancy algorithm can be used instead of the classifier considering several algorithms.

[1] P.B. Delahunt and D.H. Brainard, "Does Human Color Constancy Incorporate the Statistical Regularity of Natural Daylight?" J. Vision, vol. 4, no. 2, pp. 57-81, 2004.
[2] D.H. Foster, K. Amano, and S.M.C. Nascimento, "Color Constancy in Natural Scenes Explained by Global Image Statistics," Visual Neuroscience, vol. 23, nos. 3/4, pp. 341-349, 2006.
[3] J. Yang, R. Stiefelhagen, U. Meier, and A. Waibel, "Visual Tracking for Multimodal Human Computer Interaction," Proc. SIGCHI, pp. 140-147, 1998.
[4] T. Gevers and A. Smeulders, "Color-Based Object Recognition," Pattern Recognition, vol. 32, no. 3, pp. 453-464, 1999.
[5] T. Gevers and A.W.M. Smeulders, "Pictoseek: Combining Color and Shape Invariant Features for Image Retrieval," IEEE Trans. Image Processing, vol. 9, no. 1, pp. 102-119, Jan. 2000.
[6] M.D. Fairchild, Color Appearance Models, second ed. John Wiley & Sons, 2005.
[7] M. Ebner, Color Constancy. John Wiley & Sons, 2007.
[8] S.D. Hordley, "Scene Illuminant Estimation: Past, Present, and Future," Color Research and Application, vol. 31, no. 4, pp. 303-314, 2006.
[9] E.H. Land and J.J. McCann, "Lightness and Retinex Theory," J. Optical Soc. of Am. A, vol. 61, pp. 1-11, 1971.
[10] E.H. Land, "The Retinex Theory of Color Vision," Scientific Am., vol. 237, no. 6, pp. 108-128, Dec. 1977.
[11] G. Buchsbaum, "A Spatial Processor Model for Object Colour Perception," J. Franklin Inst., vol. 310, no. 1, pp. 1-26, July 1980.
[12] G.D. Finlayson and E. Trezzi, "Shades of Gray and Colour Constancy," Proc. IS&T/SID Color Imaging Conf., pp. 37-41, 2004.
[13] J. van de Weijer, T. Gevers, and A. Gijsenij, "Edge-Based Color Constancy," IEEE Trans. Image Processing, vol. 16, no. 9, pp. 2207-2214, Sept. 2007.
[14] D.A. Forsyth, "A Novel Algorithm for Color Constancy," Int'l J. Computer Vision, vol. 5, no. 1, pp. 5-36, 1990.
[15] G.D. Finlayson, S.D. Hordley, and P.M. Hubel, "Color by Correlation: A Simple, Unifying Framework for Color Constancy," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1209-1221, Nov. 2001.
[16] G.D. Finlayson, S.D. Hordley, and I. Tastl, "Gamut Constrained Illuminant Estimation," Int'l J. Computer Vision, vol. 67, no. 1, pp. 93-109, 2006.
[17] A. Gijsenij, T. Gevers, and J. van de Weijer, "Generalized Gamut Mapping Using Image Derivative Structures for Color Constancy," Int'l J. Computer Vision, vol. 86, nos. 2/3, pp. 127-139, 2010.
[18] D.H. Brainard and W.T. Freeman, "Bayesian Color Constancy," J. Optical Soc. of Am. A, vol. 14, pp. 1393-1411, 1997.
[19] M. D'Zmura, G. Iverson, and B. Singer, "Probabilistic Color Constancy," Geometric Representations of Perceptual Phenomena, pp. 187-202, Lawrence Erlbaum Assoc., 1995.
[20] M. Ebner, "Evolving Color Constancy," Pattern Recognition Letters, vol. 27, no. 11, pp. 1220-1229, 2006.
[21] V.C. Cardei and B.V. Funt, "Committee-Based Color Constancy," Proc. IS&T/SID Color Imaging Conf., pp. 311-313, 1999.
[22] G. Schaefer, S.D. Hordley, and G.D. Finlayson, "A Combined Physical and Statistical Approach to Colour Constancy," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 148-153, 2005.
[23] W.T. Freeman and E.H. Adelson, "The Design and Use of Steerable Filters," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 9, pp. 891-906, Sept. 1991.
[24] G. West and M.H. Brill, "Necessary and Sufficient Conditions for Von Kries Chromatic Adaptation to Give Color Constancy," J. Math. Biology, vol. 15, no. 2, pp. 249-258, 1982.
[25] G.D. Finlayson, M.S. Drew, and B.V. Funt, "Spectral Sharpening: Sensor Transformations for Improved Color Constancy," J. Optical Soc. of Am. A, vol. 11, no. 5, pp. 1553-1563, 1994.
[26] B.V. Funt and B.C. Lewis, "Diagonal versus Affine Transformations for Color Correction," J. Optical Soc. of Am. A, vol. 17, no. 11, pp. 2108-2112, 2000.
[27] K.M. Lam, "Metamerism and Colour Constancy," PhD dissertation, Univ. of Bradford, 1985.
[28] N. Moroney, M.D. Fairchild, R.W.G. Hunt, C. Li, M.R. Luo, and T. Newman, "The Ciecam02 Color Appearance Model," Proc. IS&T/SID Color Imaging Conf., pp. 23-27, 2002.
[29] J. von Kries, "Influence of Adaptation on the Effects Produced by Luminous Stimuli," Sources of Color Vision, D. MacAdam, ed., pp. 109-119, MIT Press, 1970.
[30] H.Y. Chong, S.J. Gortler, and T. Zickler, "The Von Kries Hypothesis and a Basis for Color Constancy," Proc. Int'l Conf. Computer Vision, pp. 1-8, 2007.
[31] F. Ciurea and B.V. Funt, "A Large Image Database for Color Constancy Research," Proc. IS&T/SID Color Imaging Conf., pp. 160-164, 2003.
[32] K. Barnard, V.C. Cardei, and B.V. Funt, "A Comparison of Computational Color Constancy Algorithms; Part I: Methodology and Experiments with Synthesized Data," IEEE Trans. Image Processing, vol. 11, no. 9, pp. 972-984, Sept. 2002.
[33] S.D. Hordley and G.D. Finlayson, "Reevaluation of Color Constancy Algorithm Performance," J. Optical Soc. of Am. A, vol. 23, no. 5, pp. 1008-1020, May 2006.
[34] A. Torralba and A. Oliva, "Statistics of Natural Image Categories," Network: Computation in Neural Systems, vol. 14, no. 3, pp. 391-412, 2003.
[35] J.-M. Geusebroek and A.W.M. Smeulders, "A Six-Stimulus Theory for Stochastic Texture," Int'l J. Computer Vision, vol. 62, nos. 1/2, pp. 7-16, 2005.
[36] A. Torralba, "Contextual Priming for Object Detection," Int'l J. Computer Vision, vol. 53, no. 2, pp. 169-191, 2003.
[37] J.C. van Gemert, J.M. Geusebroek, C.J. Veenman, C.G.M. Snoek, and A.W.M. Smeulders, "Robust Scene Categorization by Learning Image Statistics in Context," Proc. IEEE CVPR Workshop Semantic Learning Applications in Multimedia, June 2006.
[38] A. Oliva and A. Torralba, "Modeling the Shape of The Scene: A Holistic Representation of the Spatial Envelope," Int'l J. Computer Vision, vol. 42, no. 3, pp. 145-175, 2001.
[39] A. Gijsenij and T. Gevers, "Color Constancy Using Natural Image Statistics," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, June 2007.
[40] C.M. Bishop, Neural Networks for Pattern Recognition. Oxford Univ. Press, 1996.
[41] D.L. Ruderman, T.W. Cronin, and C.C. Chiao, "Statistics of Cone Responses to Natural Images: Implications for Visual Coding," J. Optical Soc. of Am. A, vol. 15, no. 8, pp. 2036-2045, 1998.
[42] J. van de Weijer, C. Schmid, and J.J. Verbeek, "Using High-Level Visual Information for Color Constancy," Proc. Int'l Conf. Computer Vision, 2007.
[43] J.-M. Geusebroek, "Compact Object Descriptors from Local Colour Invariant Histograms," Proc. British Machine Vision Conf., vol. 3, pp. 1029-1038, 2006.
[44] K. Barnard, L. Martin, B.V. Funt, and A. Coath, "A Data Set for Color Research," Color Research and Application, vol. 27, no. 3, pp. 147-151, 2002.
[45] A. Gijsenij, T. Gevers, and M. Lucassen, "A Perceptual Analysis of Distance Measures for Color Constancy Algorithms," J. Optical Soc. of Am. A, vol. 26, no. 10, pp. 2243-2256, 2009.
[46] K. Barnard, L. Martin, A. Coath, and B.V. Funt, "A Comparison of Computational Color Constancy Algorithms; Part II: Experiments with Image Data," IEEE Trans. Image Processing, vol. 11, no. 9, pp. 985-996, Sept. 2002.

Index Terms:
Color constancy, illuminant estimation, natural image statistics, scene semantics, computer vision.
Citation:
Arjan Gijsenij, Theo Gevers, "Color Constancy Using Natural Image Statistics and Scene Semantics," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 4, pp. 687-698, April 2011, doi:10.1109/TPAMI.2010.93
Usage of this product signifies your acceptance of the Terms of Use.