Abstract
A major goal of evolutionary art is to get images of high aesthetic value. We assume that some features of images are associated with high aesthetic value and want to find them. We have taken two image databases that have been rated by humans, a photographic database and one of abstract images generated by evolutionary art software. We have computed 55 features for each database. We have extracted two categories of rankings, the lowest and the highest. Using feature extraction methods from machine learning we have identified the features most associated with differences. For the photographic images the key features are wavelet and texture features. For the abstract images the features are colour based features.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Atkins, D., Klapaukh, R., Browne, W., Mengjie, M.: Evolution of Aesthetically Pleasing Images Without Human-In-The-Loop. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)
Datta, R., Joshi, D., Li, J., Wang, J.: Studying Aesthetics in Photographic Images Using a Computational Approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 288–301. Springer, Heidelberg (2006), http://dx.doi.org/10.1007/11744078_23
Galanter, P.: Complexism and the Role of Evolutionary Art. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution. Natural Computing Series, pp. 311–332. Springer, Heidelberg (2008)
Machado, P., Romero, J., Manaris, B.: Experiments in Computational Aesthetics. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution. Natural Computing Series, pp. 381–415. Springer, Heidelberg (2008)
McCormack, J.: Open Problems in Evolutionary Music and Art. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 428–436. Springer, Heidelberg (2005)
Nadal, M., Pearce, M.: The Copenhagen Neuroaesthetics Conference: Prospects and Pitfalls for an Emerging Field. Brain and Cognition 76(1), 172–183 (2011)
Neufeld, C., Ross, B., Ralph, W.: The evolution of artistic filters. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, pp. 335–356. Springer, Heidelberg (2007)
Newall, M.: What is a Picture?: Depiction, Realism, Abstraction. Palgrave Macmillan (2011)
Spehar, B., Clifford, C.W.G., Newell, B.R., Taylor, R.P.: Universal Aesthetic of Fractals. Computers & Graphics 27(5), 813–820 (2003)
Taylor, R.P., Spehar, B., Clifford, C.W.G., Newell, B.R.: The Visual Complexity of Pollock’s Dripped Fractals. In: Minai, A.A., Bar-Yam, Y. (eds.) Unifying Themes in Complex Systems IV, pp. 175–182. Springer, Heidelberg (2008)
Tooby, J., Cosmides, L.: Does Beauty Build Adapted Minds? Toward an Evolutionary Theory of Aesthetics, Fictions, and the Arts. Substance 30(1), 6–27 (2001)
Welsch, W.: On the Universal Appreciation of Beauty. International Yearbook of Aesthetics 12, 6–32 (2008)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
Xu, Q., D’Souza, D., Ciesielski, V.: Evolving images for entertainment. In: Proceedings of the 2007 Australasian Conference on Interactive Entertainment, December 3-5, pp. 1–8. ACM (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ciesielski, V., Barile, P., Trist, K. (2013). Finding Image Features Associated with High Aesthetic Value by Machine Learning. In: Machado, P., McDermott, J., Carballal, A. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2013. Lecture Notes in Computer Science, vol 7834. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36955-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-36955-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36954-4
Online ISBN: 978-3-642-36955-1
eBook Packages: Computer ScienceComputer Science (R0)