Who's afraid of itten: Using the art theory of color combination to analyze emotions in ABSTRACT paintings

Abstract

© 2015 ACM. Color plays an essential role in everyday life and is one of the most important visual cues in human perception. In ab-stract art, color is one of the essential means to convey the artist's intention and to affect the viewer emotionally. How-ever, colors are rarely experienced in isolation, rather, they are usually presented together with other colors. In fact, the expressive properties of two-color combinations have been extensively studied by artists. It is intriguing to try to un-derstand how color combinations in ABSTRACT paintings might affect the viewer emotionally, and to investigate if a com-puter algorithm can learn this mechanism. In this work, we propose a novel computational approach able to analyze the color combinations in ABSTRACT paint-ings and use this information to infer whether a painting will evoke positive or negative emotions in an observer. We exploit art theory concepts to design our features and the learning algorithm. To make use of the color-group informa-tion, we propose inferring the emotions elicited by paintings based on the sparse group lasso approach. Our results show that a relative improvement of between 6% and 8% can be achieved in this way. Finally, as an application, we employ our method to generate Mondrian-like paintings and do a prospective user study to evaluate the ability of our method as an automatic tool for generating ABSTRACT paintings able to elicit positive and negative emotional responses in peo-ple.