Illuminant Estimation using Ensembles of Multivariate Regression Trees
This work addresses white balancing for image processing, but it is incremental as it builds on existing ensemble methods with specific modifications.
The paper tackled the problem of white balancing in image processing by proposing a simpler and more accurate ensemble model using multivariate regression trees and direct loss minimization, leading to improved performance on diverse image sets.
White balancing is a fundamental step in the image processing pipeline. The process involves estimating the chromaticity of the illuminant or light source and using the estimate to correct the image to remove any color cast. Given the importance of the problem, there has been much previous work on illuminant estimation. Recently, an approach based on ensembles of univariate regression trees that are fit using the squared-error loss function has been proposed and shown to give excellent performance. In this paper, we show that a simpler and more accurate ensemble model can be learned by (i) using multivariate regression trees to take into account that the chromaticity components of the illuminant are correlated and constrained, and (ii) fitting each tree by directly minimizing a loss function of interest---such as recovery angular error or reproduction angular error---rather than indirectly using the squared-error loss function as a surrogate. We show empirically that overall our method leads to improved performance on diverse image sets.