Feature Selection and Novelty in Computational Aesthetics

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João Correia, Penousal Machado, Juan Romero, Adrián Carballal: Feature Selection and Novelty in Computational Aesthetics. In: EvoMUSART 2013, S. 133-144.




An approach for exploring novelty in expression-based evolutionary art systems is presented. The framework is composed of a feature extractor, a classifier, an evolutionary engine and a supervisor. The evolutionary engine exploits shortcomings of the classifier, generating misclassified instances. These instances update the training set and the classifier is re-trained. This iterative process forces the evolutionary algorithm to explore new paths leading to the creation of novel imagery. The experiments presented and analyzed herein explore different feature selection methods and indicate the validity of the approach.

Extended Abstract


booktitle={Evolutionary and Biologically Inspired Music, Sound, Art and Design},
series={Lecture Notes in Computer Science},
editor={Machado, Penousal and McDermott, James and Carballal, Adrian},
title={Feature Selection and Novelty in Computational Aesthetics},
url={http://dx.doi.org/10.1007/978-3-642-36955-1_12 http://de.evo-art.org/index.php?title=Feature_Selection_and_Novelty_in_Computational_Aesthetics },
publisher={Springer Berlin Heidelberg},
author={Correia, João and Machado, Penousal and Romero, Juan and Carballal, Adrian},

Used References

Atkins, D.L., Klapaukh, R., Browne, W.N., Zhang, M.: Evolution of aesthetically pleasing images without human-in-the-loop. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)

Baluja, S., Pomerlau, D., Todd, J.: Towards automated artificial evolution for computer-generated images. Connection Science 6(2), 325–354 (1994)

Correia, J.: Evolutionary Computation for Assessing and Improving Classifier Performance. Master’s thesis, University of Coimbra (2009)

Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40, 5:1–5:60 (2008)

Ekárt, A., Joá, A., Sharma, D., Chalakov, S.: Modelling the underlying principles of human aesthetic preference in evolutionary art. Journal of Mathematics and the Arts 6(2-3), 107–124 (2012)

Greenfield, G., Machado, P.: Simulating Artist and Critic Dynamics. In: Proceedings of the International Joint Conference on Computational Intelligence, Funchal, Madeira, Portugal, October 5-7, pp. 190–197 (2009)

Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Langley, P. (ed.) Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29-July 2, pp. 359–366. Morgan Kaufmann (2000)

den Heijer, E., Eiben, A.E.: Comparing Aesthetic Measures for Evolutionary Art. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Ebner, M., Farooq, M., Fink, A., Grahl, J., Greenfield, G., Machado, P., O’Neill, M., Tarantino, E., Urquhart, N. (eds.) EvoApplications 2010, Part II. LNCS, vol. 6025, pp. 311–320. Springer, Heidelberg (2010)

Ke, Y., Tang, X., Jing, F.: The Design of High-Level Features for Photo Quality Assessment. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 419–426 (2006)

Kowaliw, T., Dorin, A., McCormack, J.: An Empirical Exploration of a Definition of Creative Novelty for Generative Art. In: Korb, K., Randall, M., Hendtlass, T. (eds.) ACAL 2009. LNCS, vol. 5865, pp. 1–10. Springer, Heidelberg (2009)

Li, Y., Hu, C., Chen, M., Hu, J.: Investigating Aesthetic Features to Model Human Preference in Evolutionary Art. In: Machado, P., Romero, J., Carballal, A. (eds.) EvoMUSART 2012. LNCS, vol. 7247, pp. 153–164. Springer, Heidelberg (2012)

Machado, P., Romero, J., Cardoso, A., Santos, A.: Partially interactive evolutionary artists. New Generation Computing 23(42), 143–155 (2005)

Machado, P., Romero, J., Manaris, B.: Experiments in computational aesthetics: An iterative approach to stylistic change in evolutionary art. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, pp. 381–415. Springer, Heidelberg (2007)

Machado, P., Romero, J., Santos, A., Cardoso, A., Pazos, A.: On the development of evolutionary artificial artists. Computers & Graphics 31(6), 818–826 (2007)

McCormack, J.: Facing the future: Evolutionary possibilities forhuman-machine creativity. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution. Natural Computing Series, pp. 417–451. Springer (2008)

Romero, J., Machado, P.: The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Natural Computing Series. Springer, Heidelberg (2007)

Romero, J., Machado, P., Carballal, A., Correia, J.: Computing aesthetics with image judgement systems. In: McCormack, J., D’Inverno, M. (eds.) Computers and Creativity. Springer (2012)

Romero, J., Machado, P., Carballal, A., Santos, A.: Using complexity estimates in aesthetic image classification. Journal of Mathematics and the Arts 6(2-3), 125–136 (2012)

Saunders, R.: Curious Design Agents and Artificial Creativity. Ph.D. thesis, University of Sydney, Sydney, Australia (2001)

Sims, K.: Artificial Evolution for Computer Graphics. ACM Computer Graphics 25, 319–328 (1991)

Staudek, T.: Computer-aided aesthetic evaluation of visual patterns. In: ISAMA-BRIDGES Conference Proceedings, Granada, Spain, pp. 143–149 (July 2003)

Taylor, R.P., Micolich, A.P., Jonas, D.: Fractal analysis of Pollock’s drip paintings. Nature 399, 422 (1999)

Zipf, G.K.: Human Behavior and the Principle of Least-Effort. Addison-Wesley, Cambridge (1949)


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