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Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and Art Theory

Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and... Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and Art Theory ANDREZA SARTORI, DISI, University of Trento, Italy & Telecom Italia - SKIL, Trento, Italy VICTORIA YANULEVSKAYA, DISI, University of Trento, Italy ALMILA AKDAG SALAH, University of Amsterdam, the Netherlands JASPER UIJLINGS, CALVIN Group, University of Edinburgh, United Kingdom ELIA BRUNI, Free University of Bozen, Bolzano, Italy NICU SEBE, DISI, University of Trento, Italy 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and Art Theory

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/2768209
Publisher site
See Article on Publisher Site

Abstract

Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and Art Theory ANDREZA SARTORI, DISI, University of Trento, Italy & Telecom Italia - SKIL, Trento, Italy VICTORIA YANULEVSKAYA, DISI, University of Trento, Italy ALMILA AKDAG SALAH, University of Amsterdam, the Netherlands JASPER UIJLINGS, CALVIN Group, University of Edinburgh, United Kingdom ELIA BRUNI, Free University of Bozen, Bolzano, Italy NICU SEBE, DISI, University of Trento, Italy 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

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Jun 30, 2015

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