Affective Analysis of Abstract Paintings Using Statistical Analysis and Art Theory

Abstract

A novel approach to the emotion classification of abstract paintings is proposed. Based on a user study, we employ computer vision techniques to understand what makes an abstract artwork emotional. Our aim is to identify and quantify which are the emotional regions of abstract paintings, as well as the role of each feature (colour, shapes and texture) on the human emotional response. In addition, we investigate the link between the detected emotional content and the way people look at abstract paintings by using eye-tracking recordings. A bottom-up saliency model was applied to compare with eye-tracking in order to predict the emotional salient regions of abstract paintings. In future, we aim to extract metadata associated to the paintings (e.g., title, keywords, textual description, etc.) in order to correlate it with the emotional responses of the paintings. This research opens opportunity to understand why a specific painting is perceived as emotional on global and local scales.