Aesthetic Learning in an Interactive Evolutionary Art System

  • Yang Li
  • Chang-Jun Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)

Abstract

Learning aesthetic judgements is essential for reducing the users’ fatigue in evolutionary art system. Although judging beauty is a highly subjective task, we consider that certain features are important to please users. In this paper, the aesthetic preferences are explored by learning the features, which we extracted from the images in the interactive generations. In addition to color ingredients, image complexity and image order are considered highly relevant to aesthetic measurement. We interpret these two features from the information theory and fractal compression perspective. Our experimental results suggest that these features play important roles in aesthetic judgements. Our findings also show that our evolutionary art system is efficient at predicting user’s preference.

Keywords

Evolutionary art interactive evolutionary computation image complexity fractal compression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yang Li
    • 1
  • Chang-Jun Hu
    • 1
  1. 1.School of Information EngineeringUniversity of Science and Technology BeijingBeijingChina

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