Illumination Chromaticity Estimation Using Linear Learning Methods

In this paper, we present the application of two linear machine learning techniques; ridge regression and kernel regression for the estimation of illumination chromaticity. A number of machine learning techniques, neural networks and support vector machines in particular, are used to estimate the illumination chromaticity. These nonlinear approaches are shown to outperform many traditional algorithms. However, neither neural networks nor support vector machines were compared to linear regression tools in the past. We evaluate the performance of linear machine learning techniques and draw comparison with nonlinear machine learning techniques. Kernel regression achieves a mean root mean square chromaticity error of 0.052 while neural network results in 0.071. An improvement of 26% is achieved. Both quantitative and qualitative results show that the performances of the linear techniques are better when compared to nonlinear techniques on the same data set. Machine learning approaches are also compared with the gray-world and the scale by max algorithms. We perform uncertainty analysis of machine learning algorithms using a boot-strapped training data set to evaluate their consistency in the estimation of illumination chromaticity. Applications like video tracking and target detection, where illumination chromaticity estimation is important will be benefited by a better performance of linear machine learning algorithms.