|Table of Contents|

[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()
基于自适应约简相关向量机的光照色度估计
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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
基于自适应约简相关向量机的光照色度估计
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
丁二锐1;  曾平1;  姚勇2;  王义峰1
1 西安电子科技大学外部设备研究所, 西安 710071; 2 西安电子科技大学计算机信息应用研究中心, 西安 710071
Keywords:
color constancy;  illumination estimation;  chromaticity histogram;  adaptive reduced relevance vector machine
色彩一致性;  光照估计;  色度直方图;  自适应约简相关向量机
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.
提出了一种新的自适应约简相关向量机回归算法来估计图像的光照色度以达到色彩一致性目的.在稀疏贝叶斯学习的框架下, 该算法首先以多核形式自适应结合全局核函数和局部核函数扩展相关向量机, 然后应用改进的保局投影来约简多核输入矩阵的列维数以减少训练时间.为了估计光照色度, 通过图像色度直方图的模糊中心值和其相应光源值训练算法.基于真实图像的实验表明所提算法优于支持向量机和相关向量机且其训练时间小于相关向量机.

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.
Last Update: 2007-06-20