Authorship and aesthetics experiments: Comparison of results between human and computational systems

Abstract

This paper presents the results of two experiments comparing the functioning of a computational system and a group of humans when performing tasks related to art and aesthetics. The first experiment consists of the identification of a painting, while the second one uses the Maitland Graves's aesthetic appreciation test. The proposed system employs a series of metrics based on complexity estimators and low level features. These metrics feed a learning system using neural networks. The computational approach achieves similar results to those achieved by humans, thus suggesting that the system captures some of the artistic style and aesthetics features which are relevant to the experiments performed. © 2014 Springer-Verlag.