Illuminant estimation for object recognition

Comparing colour histograms of images has been shown to be a powerful technique for discriminating among large sets of images. However, these histograms depend not only on the properties of imaged objects but also on the illumination under which the objects are captured. If this illumination dependence is not accounted for prior to constructing the colour histograms, colour-based image indexing will fail when illumination changes. This failure can be addressed by correcting the RGBs in an image to corresponding RGBs representing the same scene but under a standard reference illuminant prior to constructing the histograms. To perform this correction of RGBs, it is necessary to have a measurement or, more commonly, an estimate of the illumination in the original scene. Many authors have proposed illuminant estimation (or colour constancy) algorithms to obtain such an estimate. Unfortunately, the results of colour histogram matching experiments under varying illumination conditions have shown that existing estimation algorithms do not provide a sufficiently good estimate of the scene illuminant to enable this approach to work. In this article we report on the results of our repetition of those experiments, but this time using a new illuminant estimation algorithm—the so-called color by correlation approach, which has been shown to afford significantly better performance than previous algorithms. The results of this new experiment show that when this new algorithm is used to preprocess images, a significant improvement in colour histogram matching performance is achieved. Indeed, performance is close to the theoretically optimal level of performance, that is, close to that which can be obtained using actual measurements of the scene illumination.