Aesthetic Learning in an Interactive Evolutionary Art System
Inhaltsverzeichnis
Reference
Li, Yang; Hu, Chang-Jun: Aesthetic Learning in an Interactive Evolutionary Art System. In: EvoMUSART 2010, S. 301-310.
DOI
http://link.springer.com/10.1007/978-3-642-12242-2_31
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.
Extended Abstract
Bibtex
@incollection{
year={2010},
isbn={978-3-642-12241-5},
booktitle={Applications of Evolutionary Computation},
volume={6025},
series={Lecture Notes in Computer Science},
editor={Di Chio, Cecilia and Brabazon, Anthony and Di Caro, GianniA. and Ebner, Marc and Farooq, Muddassar and Fink, Andreas and Grahl, Jörn and Greenfield, Gary and Machado, Penousal and O’Neill, Michael and Tarantino, Ernesto and Urquhart, Neil},
doi={10.1007/978-3-642-12242-2_31},
title={Aesthetic Learning in an Interactive Evolutionary Art System},
url={http://dx.doi.org/10.1007/978-3-642-12242-2_31 http://de.evo-art.org/index.php?title=Aesthetic_Learning_in_an_Interactive_Evolutionary_Art_System },
publisher={Springer Berlin Heidelberg},
keywords={Evolutionary art; interactive evolutionary computation; image complexity; fractal compression},
author={Li, Yang and Hu, Chang-Jun},
pages={301-310},
language={English}
}
Used References
Dawkins, R.: The Blind Watchmaker. Harlow Longman (1986)
Sims, K.: Artificial Evolution For Computer Graphics. In: Proc. of the 18th Annual Conference on Computer Graphics and Interactive Techniques SIGGRAPH 1991, pp. 319–328. ACM Press, New York (1991)
Lutton, E.: Evolution of Fractal shapes for artists and designers. International Journal on Artificial Intelligence Tools 15(4), 651–672 (2006)
Machado, P., Cardoso, A.: All the Truth About NEvAr. In: Corne, D.P., Bently (eds.) Applied Intelligence, Special issue on Creative Systems, vol. 16(2), pp. 101–119. Kluwer Academic Publishers, Dordrecht (2002)
Wang, S.F., Wang, S., Takagi, H.: User Fatigue Reduction by an Absolute Rating Data-trained Predictor in IEC. In: Proc. IEEE Congress on Evolutionary Computation, pp. 2195–2200. IEEE Press, New York (2006)
Takagi, H.: Interactive Evolutionary Computation. In: Proc. of the 5th International Conference on Soft Computing and Information / Intelligent Systems, Iizuka, Japan, pp. 41–50 (1998)
Birkhoff, G.D.: Aesthetic Measure. Harvard University Press, Cambridge (1933)
Rigau, J., Feixas, M., Sbert, M.: Informational Aesthetics Measures. In: Proc. IEEE Computer society, pp. 24–34 (2008)
Schmidhuber, J.: Low-complexity art. Leonardo. Journal of the International Society for the Arts, Sciences, and Technology 30(2), 97–103 (1997)
Scha, R., Bod, R.: Informatie en Informatiebeleid 11(1), 54–63 (1993), English translation http://iaaa.nl/rs/compestE.html
Machado, P., Romero, J., Manaris, B.: Experiments in Computational Aesthetics: An Iterative Approach to Stylistic Change in Evolutionary Art. In: The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, pp. 381–415. Springer, Heidelberg (2008)
Witten, H.I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, San Francisco (2000)
Links
Full Text
[extern file]
