Estimation of illumination chromaticity via adaptive reduced relevance vector machine  

基于自适应约简相关向量机的光照色度估计(英文)

作　　者：丁二锐[1] 曾平[1] 姚勇[2] 王义峰[1] 

机构地区：[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

出　　处：《Journal of Southeast University(English Edition)》2007年第2期202-205,共4页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No60573139);the Innovation Foundation of Xidian University forGraduates (No05008)

摘　　要：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.

关 键 词：color constancy illumination estimation chromaticity histogram adaptive reduced relevance vector machine 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]