Corrected-Moment Illuminant Estimation

Image colors are biased by the color of the prevailing illumination. As such the color at pixel cannot always be used directly in solving vision tasks from recognition, to tracking to general scene understanding. Illuminant estimation algorithms attempt to infer the color of the light incident in a scene and then a color cast removal step discounts the color bias due to illumination. However, despite sustained research since almost the inception of computer vision, progress has been modest. The best algorithms - now often built on top of expensive feature extraction and machine learning - are only about twice as good as the simplest approaches. This paper, in effect, will show how simple moment based algorithms - such as Gray-World - can, with the addition of a simple correction step, deliver much improved illuminant estimation performance. The corrected Gray-World algorithm maps the mean image color using a fixed (per camera) 3x3 matrix transform. More generally, our moment approach employs 1st, 2nd and higher order moments - of colors or features such as color derivatives - and these again are linearly corrected to give an illuminant estimate. The question of how to correct the moments is an important one yet we will show a simple alternating least-squares training procedure suffices. Remarkably, across the major datasets - evaluated using a 3-fold cross validation procedure - our simple corrected moment approach always delivers the best results (and the performance increment is often large compared with the prior art). Significantly, outlier performance was found to be much improved.