[1] Ding Errui, Zeng Ping, Yao Yong, Wang Yifeng, et al. Estimation of illumination chromaticityvia adaptive reduced relevance vector machine [J]. Journal of Southeast University (English Edition), 2007, 23 (2): 202-205. [doi:10.3969/j.issn.1003-7985.2007.02.010]

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Estimation of illumination chromaticityvia adaptive reduced relevance vector machine()

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

Volumn:

23

Issue:

2007 2

Page:

202-205

Research Field:

Computer Science and Engineering

Publishing date:

2007-06-30

Info

Title:

Estimation of illumination chromaticityvia adaptive reduced relevance vector machine

Author(s):

Ding Errui¹;  Zeng Ping¹;  Yao Yong²;  Wang Yifeng¹

¹Research Institute of Peripherals, Xidian University, Xi’an 710071, China
² Research Center of Computer Information Applications, Xidian University, Xi’an 710071, China

Keywords:

color constancy;  illumination estimation;  chromaticity histogram;  adaptive reduced relevance vector machine

PACS:

TP391;TP181

DOI:

10.3969/j.issn.1003-7985.2007.02.010

Abstract:

A new regression algorithm of an adaptive reduced relevance vector machine is proposed to estimate the illumination chromaticity of an image for the purpose of color constancy.Within the framework of sparse Bayesian learning, the algorithm extends the relevance vector machine by combining global and local kernels adaptively in the form of multiple kernels, and the improved locality preserving projection(LLP)is then applied to reduce the column dimension of the multiple kernel input matrix to achieve less training time.To estimate the illumination chromaticity, the algorithm is trained by fuzzy central values of chromaticity histograms of a set of images and the corresponding illuminants.Experiments with real images indicate that the proposed algorithm performs better than the support vector machine and the relevance vector machine while requiring less training time than the relevance vector machine.

References:

[1] Lin Chin-Teng, Fan Kan-Wei, Cheng Wen-Chang.An illumination estimation scheme for color constancy based on chromaticity histogram and neural network[C]//Proceedings of 2005 International Conference on Systems, Man and Cybernetics.Hawaii, USA, 2005:2488-2494.
[2] Cardei Vlad C, Funt Brian, Barnard Kobus.Estimating the scene illumination chromaticity by using a neural network [J].Journal of the Optical Society of American A:Optics and Image Science, and Vision, 2002, 19(12):2374-2386.
[3] Xiong Weihua, Funt Brian.Estimating illumination chromaticity via support vector regression [J].Journal of Imaging Science and Technology, 2006, 50(4):341-348.
[4] Barnard Kobus, Cardei Vlad, Funt Brian.A comparison of computational color constancy algorithms—part Ⅰ:methodology and experiments with synthesized data [J].IEEE Transactions on Imaging Processing, 2002, 11(9):972-984.
[5] Tipping M E.Sparse Bayesian learning and the relevance vector machine [J].Journal of Machine Learning Research, 2001, 1(3):211-244.
[6] Smits G F, Jordaan E M.Improved SVM regression using mixtures of kernels[C]//Proceedings of the International Joint Conference on Neural Networks.Honolulu, 2002:2785-2790.
[7] He Xiaofei.Locality preserving projections [D].Chicago:The University of Chicago, 2005.

Memo

Memo:

Biographies: Ding Errui(1980—), male, graduate;Zeng Ping(corresponding author), male, professor, zp8637@126.com.

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