Art's Hidden Topology: A window into human perception

Abstract

Generations of researchers have sought a link between features of an artistic image and the audiences experience. However, a direct link between the properties of an image and the responses evoked has still not been established. Given the importance of shape to human perception and artistic creation, it can be assumed that one of the most important aspects of an artistic image is the use of different visual structures. We show that a method from the field of computational topology, persistent homology, can be used to analyse properties of image structures and composition at multiple scales. In order to determine the reliability of this method as a tool for analysing visual artworks, we analysed two different sets of abstract paintings that revealed significant discrepancies in the eye tracking and electroencephalography (EEG) activity of viewers. Our research showed that our newly developed method using persistent homology, not only clearly distinguished between two sets of images, which was not possible with common statistical image properties, but also allowed us to map topological features onto gaze fixation heat maps.