Feature Selection and Novelty in Computational Aesthetics

  • João Correia
  • Penousal Machado
  • Juan Romero
  • Adrian Carballal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7834)


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.


Feature Selection Feature Extractor Feature Selection Method Content Base Image Retrieval Evolutionary Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • João Correia
    • 1
  • Penousal Machado
    • 1
  • Juan Romero
    • 2
  • Adrian Carballal
    • 2
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.Faculty of Computer ScienceUniversity of A CoruñaCoruñaSpain

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