In consumer imaging applications involving photo collages or composition of user photos with professional artwork, inconsistent color appearance of photos and artwork from different sources can result in compositions that do no look aesthetically pleasing. Users often express a desire to modify individual images to achieve a more consistent color appearance. Prior work in color transfer that extract the color properties of one image and apply it to another have shown very interesting results [1,2]. These works focused on achieving an artistic effect, usually without the constraint of conserving object color. In consumer imaging, we have to be more conscious about conserving general object color and especially skin tones, which are not amenable to aggressive color change. In this paper we describe an algorithm to estimate the color and tone properties of an image and transfer these properties to another image under a strong naturalness constraint. In our method, color changes are constrained to correspond to incomplete adaptation under natural illuminants. We use a simple Bayesian method to characterize scene color properties, expressed as scene color temperature and illumination levels. An existing color adaptation model RLAB [3] is used to apply color changes by simulating incomplete adaptation to a colored target illuminant. We emphasize that this is not a method of white point estimation nor a white balance procedure Rather, we use color adaptation models as a means to ensure color adjustments to be “plausible”, and therefore maintain a natural appearance to the images even after significant color adjustments.