Estimating Illumination Chromaticity via Kernel Regression

We propose a simple nonparametric linear regression tool, known as kernel regression (KR), to estimate the illumination chromaticity. We design a Gaussian kernel whose bandwidth is selected empirically. Previously, nonlinear techniques like neural networks (NN) and support vector machines (SVM) are applied to estimate the illumination chromaticity. However, neither of the techniques was compared with linear regression tools. We show that the proposed method performs better chromaticity estimation compared to NN, SVM, and linear ridge regression (RR) approach on the same data set.