Evaluation of automatic image color theme extraction methods

Color themes are quite important in several fields from visual and graphic art design to image analysis and manipulation. Color themes can be extracted from an image manually by humans or automatically by a software. Plenty of automatic color theme extraction methods, either supervised or unsupervised, have been presented in the state of the art in the last years. Evaluation of a color theme goodness with respect to a reference one is based on visual and subjective comparisons, that may be affected by cultural and social aspects, they are time consuming and not costless. In this paper we experiment several supervised and unsupervised state-of-the-art methods for color theme extraction. To overcome the burden of a subjective evaluation, we experiment the use of a computational metric based on the Earth Mover’s distance for goodness evaluation instead of a subjective one. Results show the best color theme is extracted by using a supervised method based on a regression model trained on user-defined color themes and that the computational metric adopted is comparable to a subjective one.