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== Bibtex ==  
 
== Bibtex ==  
 +
@incollection{
 +
year={2013},
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isbn={978-3-642-36954-4},
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booktitle={Evolutionary and Biologically Inspired Music, Sound, Art and Design},
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volume={7834},
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series={Lecture Notes in Computer Science},
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editor={Machado, Penousal and McDermott, James and Carballal, Adrian},
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doi={10.1007/978-3-642-36955-1_5},
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title={Finding Image Features Associated with High Aesthetic Value by Machine Learning},
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url={http://dx.doi.org/10.1007/978-3-642-36955-1_5 http://de.evo-art.org/index.php?title=Finding_Image_Features_Associated_with_High_Aesthetic_Value_by_Machine_Learning },
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publisher={Springer Berlin Heidelberg},
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keywords={Evolutionary Art; Genetic Art; Feature Extraction; Feature Selection},
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author={Ciesielski, Vic and Barile, Perry and Trist, Karen},
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pages={47-58},
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language={English}
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}
  
 
== Used References ==
 
== Used References ==

Aktuelle Version vom 1. November 2015, 22:35 Uhr


Referenz

Victor Ciesielski, Perry Barile, Karen Trist: Finding Image Features Associated with High Aesthetic Value by Machine Learning. In: EvoMUSART 2013, S. 47-58.

DOI

http://link.springer.com/10.1007/978-3-642-36955-1_5

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.

Extended Abstract

Bibtex

@incollection{
year={2013},
isbn={978-3-642-36954-4},
booktitle={Evolutionary and Biologically Inspired Music, Sound, Art and Design},
volume={7834},
series={Lecture Notes in Computer Science},
editor={Machado, Penousal and McDermott, James and Carballal, Adrian},
doi={10.1007/978-3-642-36955-1_5},
title={Finding Image Features Associated with High Aesthetic Value by Machine Learning},
url={http://dx.doi.org/10.1007/978-3-642-36955-1_5 http://de.evo-art.org/index.php?title=Finding_Image_Features_Associated_with_High_Aesthetic_Value_by_Machine_Learning },
publisher={Springer Berlin Heidelberg},
keywords={Evolutionary Art; Genetic Art; Feature Extraction; Feature Selection},
author={Ciesielski, Vic and Barile, Perry and Trist, Karen},
pages={47-58},
language={English}
}

Used 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)


Links

Full Text

http://www.cs.rmit.edu.au/~vc/papers/evomusart13.pdf

intern file

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