digital photography

Illuminant estimation using ensembles of multivariate regression trees

In this paper, we show that a simple and 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.