Scene analysis assisting for AWB using binary decision trees and average image metrics

We propose a technique for improving Automatic White Balance (AWB) settings in digital cameras on the basis automatic classification of image fragments in pictures. Our approach is based on constructing binary decision trees and using them as decision-making devices for identifying and locating patches of consistent texture in an image, such as grass, sky etc. We demonstrate with examples that this approach can be applied successfully to enhance color reproduction of images in challenging light conditions. Furthermore, due to low levels of false-positives, the method can be used in combination with any other AWB algorithms that do not rely on color clues obtained from the inference and analysis of content in images taken.