Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Applied Intelligence
Modelling human preference in evolutionary art
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Evolving a library of artistic scene descriptors
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Investigating aesthetic features to model human preference in evolutionary art
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Learning aesthetic judgements in evolutionary art systems
Genetic Programming and Evolvable Machines
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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.