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Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and Art Theory by Andreza Sartori; Victoria Yanulevskaya; Almila Akdag Salah; Jasper Uijlings; Elia Bruni; Nicu Sebe is a Computer Science article available to read on EtoBox.

What is Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and Art Theory about?

When artists express their feelings through the artworks they create, it is believed that the resulting works transform into objects with “emotions” capable of conveying the artists' mood to the audience. There is little to no dispute about this belief: Regardless of the artwork, genre, time, and origin of creation, people from different backgrounds are able to read the emotional messages. This holds true even for the most abstract paintings. Could this idea be applied to machines as well? Can machines learn what makes a work of art “emotional”? In this work, we employ a state-of-the-art recognition system to learn which statistical patterns are associated with positive and negative emotions on two different datasets that comprise professional and amateur abstract artworks. Moreover, we analyze and compare two different annotation methods in order to establish the ground truth of positive and negative emotions in abstract art. Additionally, we use computer vision techniques to quantify which parts of a painting evoke positive and negative emotions. We also demonstrate how the quantification of evidence for positive and negative emotions can be used to predict which parts of a paint

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Author

Andreza Sartori; Victoria Yanulevskaya; Almila Akdag Salah; Jasper Uijlings; Elia Bruni; Nicu Sebe

Publisher

ACM

Published

2015

Language

EN

Field

Computer Science (Physical Sciences)