Affective Analysis of Abstract Paintings Using Statistical Analysis and Art Theory

This research thesis aims to provide a novel approach to Emotion Recognition of Images: based on empirical studies, we employ the state-of-the-art computer vision techniques in order to understand what makes an abstract artwork emotional. We identify and quantify the emotional regions of abstract paintings. We also investigate the contributions of the main aspects present on abstract artworks (i.e., colour, shape and texture) to automatically predict emotional valence of them. By using eye-tracking recordings we investigate the link between the detected emotional content and the way people look at abstract paintings. We apply a bottom-up saliency model to compare with eye-tracking in order to predict the emotional salient regions of abstract paintings. Finally, we use the metadata associated to the paintings (e.g., title, description and/or artist statement) and correlate it with the emotional responses of the paintings. This research opens opportunity to understand why an abstract painting is perceived as emotional from global and local scales. Moreover, this work provides to art historians and art researches with a new perspective on the analysis of abstract paintings.