[1] Wu Meng, Luo Kai,. Color constancy using color edge momentsand regularized regression in anchored neighborhood [J]. Journal of Southeast University (English Edition), 2016, 32 (4): 426-431. [doi:10.3969/j.issn.1003-7985.2016.04.006]

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Color constancy using color edge momentsand regularized regression in anchored neighborhood()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

32

Issue:

2016 4

Page:

426-431

Research Field:

Computer Science and Engineering

Publishing date:

2016-12-20

Info

Title:

Color constancy using color edge momentsand regularized regression in anchored neighborhood

Author(s):

Wu Meng;  Luo Kai

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Keywords:

color constancy;  color edge moments;  anchored neighborhood;  nearest neighbor

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2016.04.006

Abstract:

To improve the accuracy of illumination estimation while maintaining a relative fast execution speed, a novel learning-based color constancy using color edge moments and regularized regression in an anchored neighborhood is proposed. First, scene images are represented by the color edge moments of various orders. Then, an iterative regression with a squared Frobenius norm(F-norm)regularizer is introduced to learn the mapping between the edge moments and illuminants in the neighborhood of the anchored sample. Illumination estimation for the test image finally becomes the nearest anchored point search followed by a matrix multiplication using the associated mapping matrix which can be precalculated and stored. Experiments on two standard image datasets show that the proposed approach significantly outperforms the state-of-the-art algorithms with a performance increase of at least 10.35% and 7.44% with regard to median angular error.

References:

[1] Gijsenij A, Gevers T, van de Weijer J. Computational color constancy: Survey and experiments [J]. IEEE Transactions on Image Processing, 2011, 20(9): 2475-2489. DOI:10.1109/TIP.2011.2118224.
[2] Joze H R V, Drew M S. Exemplar-based color constancy and multiple illumination [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(5): 860-873. DOI:10.1109/tpami.2013.169.
[3] Buchsbaum G. A spatial processor model for object color perception [J]. Journal of the Franklin Institute, 1980, 310(1): 1-26. DOI:10.1016/0016-0032(80)90058-7.
[4] Land E H. The retinex theory of color vision [J]. Scientific American, 1977, 237(6): 108-128. DOI:10.1038/scientificamerican1277-108.
[5] Van de Weijer J, Gevers T, Gijsenij A. Edge-based color constancy [J]. IEEE Transactions on Image Processing, 2007, 16(9): 2207-2214. DOI:10.1109/tip.2007.901808.
[6] Ying X, Hou L, Hou Y, et al. Canonicalized central absolute moment for edge-based color constancy [C]//IEEE International Conference on Image Processing. Melbourne, Australia, 2013: 2260-2263. DOI:10.1109/icip.2013.6738466.
[7] Gijsenij A, Gevers T, van de Weijer J. Improving color constancy by photometric edge weighting [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(5): 918-929. DOI:10.1109/TPAMI.2011.197.
[8] Gao S, Han W, Yang K, et al. Efficient color constancy with local surface reflectance statistics [C]//European Conference on Computer Vision. Zurich, Switzerland, 2014: 158-173. DOI:10.1007/978-3-319-10605-2_-11.
[9] Gao S B, Yang K F, Li C Y, et al. Color constancy using double-opponency [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(10): 1973-1985. DOI:10.1109/TPAMI.2015.2396053.
[10] Gehler P V, Rother C, Blake A, et al. Bayesian color constancy revisited [C]//IEEE International Conference on Computer Vision and Pattern Recognition. Anchorage, Alaska, USA, 2008: 4587765-1-4587765-8. DOI:10.1109/cvpr.2008.4587765.
[11] Xiong W, Funt B. Estimating illumination chromaticity via support vector regression [J]. Journal of Imaging Science and Technology, 2006, 50(4): 341-348. DOI:10.2352/j.imagingsci.technol.(2006)50:4(341).
[12] Cheng D, Prasad D K, Brown M S. Illuminant estimation for color constancy: Why spatial-domain methods work and the role of the color distribution [J]. Journal of the Optical Society of America A, 2014, 31(5): 1049-1058. DOI:10.1364/josaa.31.001049.
[13] Shi L, Xiong W, Funt B. Illuminant estimation via thin-plate spline interpolation [J]. Journal of the Optical Society of America A, 2011, 28(5): 940-948. DOI:10.1364/josaa.28.000940.
[14] Gijsenij A, Gevers T. Color constancy using natural image statistics and scene statistics [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(4): 687-698. DOI:10.1109/TPAMI.2010.93.
[15] Finlayson G D. Corrected-moment illuminant estimation [C]//IEEE International Conference on Computer Vision. Sydney, Australia, 2013: 1904-1911. DOI:10.1109/iccv.2013.239.
[16] Timofte R, de Smet V, van Gool L. Anchored neighborhood regression for fast example-based super-resolution [C]//IEEE International Conference on Computer Vision. Sydney, Australia, 2013: 1920-1927. DOI:10.1109/iccv.2013.241.
[17] Mindru F, Tuytelaars T, van Gool L, et al. Moment invariants for recognition under changing viewpoint and illumination [J]. Computer Vision and Image Understanding, 2004, 94(1): 3-27. DOI:10.1016/j.cviu.2003.10.011.
[18] Ciurea F, Funt B. A large image database for color constancy research [C]//Proceedings of the Eleventh Color Imaging Conference. Scottsdale, Arizona, USA, 2003: 160-164.

Memo

Memo:

Biography: Wu Meng(1984—), female, doctor, lecturer, wumeng@nwpu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61503303, 51409215), the Fundamental Research Funds for the Central Universities(No.G2015KY0102).
Citation: Wu Meng, Luo Kai.Color constancy using color edge moments and regularized regression in anchored neighborhood[J].Journal of Southeast University(English Edition), 2016, 32(4):426-431.DOI:10.3969/j.issn.1003-7985.2016.04.006.

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