Example-based color stylization based on categorical perception

We describe a new computational approach to stylize the colors of an image by using a reference image. During processing, we take characteristics of human color perception into account to generate more appealing results. Our system starts by classifying each pixel value into one of a set of the basic color categories, derived from our psycho-physiological experiments. The basic color categories are perceptual categories that are universal to everyone, regardless of nationality or cultural background. These categories provide restrictions on the color transformations to avoid generating unnatural results. Our system then renders a new image by transferring colors from a reference image to the input image, based on this categorizations. To avoid artifacts due to the explicit clustering, our system defines fuzzy categorization when pseudo-contours appear in the resulting image. We present a variety of results and show that our color transformation performs a large, yet natural color transformation without any sense of incongruity, and that the resulting images automatically capture the characteristics of the color use of the reference image.