Physics-based illuminant color estimation as an image semantics clue

Most algorithms for extracting illuminant chromaticity from arbitrary images, such as the images found on the web, are based on machine learning techniques. We will show how a physics-based methodology can be adapted to provide relative illumination information on real images. More specifically, we use the inverse-intensity chromaticity representation and show how the analysis of the histograms of illumination-chromaticity candidates provides information about the type of illumination(s) present in a scene. Experiments indicate that the estimate is quite robust towards noise, and that simple measurements on the histogram peak can be used to counter-check the reliability of the estimate.