Computational color constancy

Computational color constancy aims to estimate the actual color in an acquired scene disregarding its illuminant. Many illuminant estimation solutions have been proposed in the last few years, although it is known that the problem addressed is actually ill-posed as its solution lacks uniqueness and stability. To cope with this problem, different solutions usually exploit some assumptions about the statistical properties of the expected illuminants and/or of the object reflectances in the scene. In this keynote we briefly review state of the art methods and illustrate our recent research on classification-based color constancy, where automatically extracted low level features are used to drive the selection and combination of the best algorithm(s) for each image. We describe how the problem of illuminant estimation and correction is deeply intertwined with the one of color space transformation. Finally, we also highlight research trends in these fields.