Feature Selection and Novelty in Computational Aesthetics

Authors

Abstract

An approach for exploring novelty in expression-based evolutionary art systems is presented. The framework is composed of a feature extractor, a classifier, an evolutionary engine and a supervisor. The evolutionary engine exploits shortcomings of the classifier, generating misclassified instances. These instances update the training set and the classifier is re-trained. This iterative process forces the evolutionary algorithm to explore new paths leading to the creation of novel imagery. The experiments presented and analyzed herein explore different feature selection methods and indicate the validity of the approach.

Conference

Evolutionary and Biologically Inspired Music, Sound, Art and Design - Second International Conference, EvoMUSART 2013, Vienna, Austria, April 3-5, 2013. Proceedings, volume 7834 of Lecture Notes in Computer Science, pages 133-144, Springer, April 2013