Evolving Art using Measures for Symmetry, Compositional Balance and Liveliness

Eelco den Heijer

2012

Abstract

In this paper we present our research into the unsupervised evolution of aesthetically pleasing images using measures for symmetry, compositional balance and liveliness. We evolve images without human aesthetic evaluation, and use measures for symmetry, compositional balance and liveliness as fitness functions. Our symmetry measure calculates the difference in intensity of opposing pixels around one or more axes. Our measure of compositional balance calculates the similarity between two parts of an image using a color image distance function. Using the latter measure, we are able to evolve images that show a notion of ‘balance’ but are not necessarily symmetrical. Our measure for liveliness uses the entropy of the intensity of the pixels of the image. We performed a number of experiments in which we evolved aesthetically pleasing images using the aesthetic measures, in order to evaluate the effect of each fitness function on the resulting images. We also performed an experiment using a combination of aesthetic measures using a multi-objective evolutionary algorithm (NSGA-II).

References

  1. Arnheim, R. (1988). The power of the center : a study of composition in the visual arts. University of California Press.
  2. Baluja, S., Pomerleau, D., and Jochem, T. (1994). Towards automated artificial evolution for computer-generated images. Connection Science, 6:325-354.
  3. Bauerly, M. P. and Liu, Y. (2005). Development and validation of a symmetry metric for interface aesthetics. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 49(5):681-685.
  4. Bauerly, M. P. and Liu, Y. (2008). Effects of symmetry and number of compositional elements on interface and design aesthetics. International Journal of HumanComputer Interaction, 24(3):275-287.
  5. Bentley, P. J. and Corne, D. W., editors (2001). Creative Evolutionary Systems. Morgan Kaufmann, San Mateo, California.
  6. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6:182-197.
  7. den Heijer, E. and Eiben, A. (2010a). Comparing aesthetic measures for evolutionary art. In Applications of Evolutionary Computation, pages 311-320.
  8. den Heijer, E. and Eiben, A. (2010b). Using aesthetic measures to evolve art. In IEEE Congress on Evolutionary Computation, pages 311-320.
  9. den Heijer, E. and Eiben, A. (2011). Evolving art using multiple aesthetic measures. In EvoApplications, LNCS 6625, 2011, pages 234-243.
  10. den Heijer, E. and Eiben, A. (2012). Evolving pop art using scalable vector graphics. In EvoMusart 2012, Evolutionary and Biologically Inspired Music, Sound, Art and Design, LNCS 7247, pages 48-59, Malaga, Spain.
  11. Dutton, D. (2009). The Art Instinct. Oxford University Press.
  12. Eiben, A. E. and Smith, J. E. (2008). Introduction to Evolutionary Computing (Natural Computing Series). Springer.
  13. Etcoff, N. (1999). Survival of the prettiest: the science of beauty. Anchor Books.
  14. Greenfield, G. R. (2003). Evolving aesthetic images using multiobjective optimization. In Proceedings of the 2003 Congress on Evolutionary Computation CEC 2003, pages 1903-1909. IEEE Press.
  15. Ling, D. N. C., Samsudin, A., and Abdullah, R. (2000). Aesthetic measures for assessing graphic screens. J. Inf. Sci. Eng., 16(1):97-116.
  16. Locher, P. and Nodine, C. (1989). The perceptual value of symmetry. Computers & Mathematics with Applications, 17(4-6):475-484.
  17. Machado, P. and Cardoso, A. (1998). Computing aesthetics. In Proceedings of the Brazilian Symposium on Artificial Intelligence, SBIA-98, pages 219-229. SpringerVerlag.
  18. Machado, P. and Cardoso, A. (2002). All the truth about nevar. Applied Intelligence, 16(2):101-118.
  19. Matkovic, K., Neumann, L., Neumann, A., Psik, T., and Purgathofer, W. (2005). Global contrast factor-a new approach to image contrast. In Neumann, L., Sbert, M., Gooch, B., and Purgathofer, W., editors, Computational Aesthetics, pages 159-168. Eurographics Association.
  20. Reber, R., Schwarz, N., and Winkielman, P. (2004). Processing fluency and aesthetic pleasure: Is beauty in the perceiver's processing experience? Personality and Social Psychology Review, 8(4):364-382.
  21. Rigau, J., Feixas, M., and Sbert, M. (2008). Informational aesthetics measures. IEEE Computer Graphics and Applications, 28(2):24-34.
  22. Romero, J. and Machado, P., editors (2007). The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Natural Computing Series. Springer Berlin Heidelberg.
  23. Rooke, S. (2001). Eons of genetically evolved algorithmic images. In (Bentley and Corne, 2001), pages 339-365.
  24. Ross, B., Ralph, W., and Zong., H. (2006). Evolutionary image synthesis using a model of aesthetics. In IEEE Congress on Evolutionary Computation (CEC) 2006, pages 1087-1094.
  25. Sims, K. (1991). Artificial evolution for computer graphics. SIGGRAPH 7891: Proceedings of the 18th annual conference on Computer graphics and interactive techniques, 25(4):319-328.
  26. Stricker, M. and Orengo, M. (1995). Similarity of color images. In Storage and Retrieval of Image and Video Databases III, Vol. 2, pages 381-392.
  27. Weyl, H. (1983). Symmetry. Princeton University Press.
  28. White, A. (2011). The Elements of Graphic Design (Second Edition). Allworth Press.
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Paper Citation


in Harvard Style

den Heijer E. (2012). Evolving Art using Measures for Symmetry, Compositional Balance and Liveliness . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 52-61. DOI: 10.5220/0004149600520061


in Bibtex Style

@conference{ecta12,
author={Eelco den Heijer},
title={Evolving Art using Measures for Symmetry, Compositional Balance and Liveliness},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)},
year={2012},
pages={52-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004149600520061},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)
TI - Evolving Art using Measures for Symmetry, Compositional Balance and Liveliness
SN - 978-989-8565-33-4
AU - den Heijer E.
PY - 2012
SP - 52
EP - 61
DO - 10.5220/0004149600520061