Abstract Painting with Interactive Control of Perceptual Entropy

This article presents a framework for generating abstract art from photographs. The aesthetics of abstract art is largely attributed to its greater perceptual ambiguity than photographs. According to psychological theories [Berlyne 1971], the ambiguity tends to invoke moderate mental effort in the viewer for interpreting the underlying contents, and this process is usually accompanied by subtle aesthetic pleasure. We study this phenomenon through human experiments comparing the subjects' interpretations of abstract art and photographs, and quantitatively verify, the increased perceptual ambiguities in terms of recognition accuracy and response time. Based on the studies, we measure the level of perceptual ambiguity using entropy, as it measures uncertainty levels in information theory, and propose a painterly rendering method with interactive control of the ambiguity levels. Given an input photograph, we first segment it into regions corresponding to different objects and parts in an interactive manner and organize them into a hierarchical parse tree representation. Then we execute a painterly rendering process with image obscuring operators to transfer the photograph into an abstract painting style with increased perceptual ambiguities in both the scene and individual objects. Finally, using kernel density estimation and message-passing algorithms, we compute and control the ambiguity levels numerically to the desired levels, during which we may predict and control the viewer's perceptual path among the image contents by assigning different ambiguity levels to different objects. We have evaluated the rendering results using a second set of human experiments, and verified that they achieve similar abstract effects to original abstract paintings.