JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2016, Vol. 51 ›› Issue (1): 101-105.doi: 10.6040/j.issn.1671-9352.0.2015.357

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Color constancy using hierarchy segments

WANG Lei1, XIE Jiang-ning2   

  1. 1. School of Computer Engineering, Weifang University, Weifang 261061, Shandong, China;
    2. School of Computer Science and Technology, Shandong University, Jinan 250101, Shandong, China
  • Received:2015-07-21 Online:2016-01-16 Published:2016-11-29

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Abstract: Color constancy is the ability to measure colors of objects independent of the color of the light source. A well-known color constancy method is based on the gray-world assumption which assumes that the average reflectance of surfaces in the world is achromatic. In this paper, instead of applying gray world method on the entire image, the images was segment into a lot of segments using a hierarchical segmentation method, and the simple gray world approach was applied on each segments. Then, estimated results from different segments were clustered together to get a final result. Experiment results show that the proposed algorithm outperforms the original gray world method. The mean error is reduced to 36.0% with respect to the original gray world method and the median error is reduced to 63.5%. Our method outperforms state-of-the-art color constancy algorithms and produces comparable results with the best published scores on the same dataset.

Key words: hierarchy segments, Bayes, color constancy, image segments

CLC Number: 

  • TP391
[1] YANG J, STIEFELHAGEN R, MEIER U,WAIBEL A. Visual tracking formulti-modal human computer interaction[C] // Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM Press, 1998: 140-147.
[2] GEVERS T, SMEULDERS A W M. Pictoseek: Combining colour and shape invariant features for image retrieval[J]. IEEE Transactions on Image Processing, 2000, 9(1):102-119.
[3] FAIRCHILD M D. Color appearance models[J]. Packaging Engineering, 2005, 552(3):346.
[4] BUCHSBAUM G. A spatial processor model for object colour perception[J]. Journal of the Franklin Institute, 1980, 310(80):1-26.
[5] BARNARD K, FUNT B, CARDEI V. A comparison of computational color constancy algorithms, part one; theory and experiments with synthetic data[J]. IEEE Transactions on Image Processing, 2002, 11:972-984.
[6] 赵萍,王文举,陈伟.分层色彩校正算法研究[J].计算机工程与应用,2015,51(6):158-162. ZHAO Ping, WANG Wenju, CHEN Wei. Research on hierarchical color correction algorithm[J]. Computer Engineering and Application, 2015, 51(6):158-162.
[7] 张明,白松浩,邢姗姗.基于金字塔分解的自动色彩均衡算法[J].计算机工程,2015,(2):215-218. ZHANG Ming, BAI Songhao, XING Shanshan. Automatic color equalization algorithm based on pyramid decomposition[J]. Computer Engineering, 2015,(2):215-218.
[8] VAN DE SANDE K E A, UIJLINGS J R R, GEVERS T, et al. Segmentation as selective search for object recognition[C] // Proceedings of IEEE International Conference on Computer Vision. New York: IEEE, 2011: 1879-1886.
[9] SHI J, MALIK J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2000, 22(8):888-905.
[10] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient graph-based image segmentation[J]. International Journal of Computer Vision, 2004, 59(2):167-181.
[11] COMANICIU D, MEER P. Mean shift: a robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5):603-619.
[12] GEHLER P V, ROTHER C, BLAKE A, et al. Bayesiancolor constancy revisited[C] // Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2008: 1-8.
[13] LU R, GIJSENIJ A, GEVERS T, et al. Color constancy using 3D scene geometry[J]. IEEE International Conference on Computer Vision, 2009, 30(2):1749-1756.
[14] GIJSENIJ A, GEVERS T, WEIJER J V D. Generalized gamut mapping using image derivative structures for color constancy[J]. International Journal of Computer Vision, 2010, 86(2-3):127-139.
[15] GIJSENIJ A, GEVERS T. Color constancy using natural image statistics and scene semantics[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(4):687-698.
[16] LAND E H. The retinex theory of color vision[J]. Scientific American, 1977, 237(6):108-128.
[17] FINLAYSON G, TREZZI E. Shades of gray and colour constancy [C] // Proceedings of the 12th Color Imaging Conference: Color Science And Engineering Systems, Technologies, Applications.Springfield:SOC Imaging Science & Technology, 2004: 37-41.
[18] WEIJER J V D, GEVERS T, GIJSENIJ A. Edge-based color constancy[J]. IEEE Transactions on Image Processing, 2007, 16(9):2207-2214.
[19] FORSYTH D A. A novel algorithm for color constancy[J]. International Journal of Computer Vision, 1990, 5(1):5-35.
[20] SHI L, FUNT B. Re-processed version of the gehler color constancy dataset of 568 images[EB/OL].[2015-02-14]. http://www.cs.sfu.ca/~colour/data/
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