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Learning aesthetic judgements in evolutionary art systems

Abstract

Learning aesthetic judgements is essential for reducing users’ fatigue in evolutionary art systems. Although judging beauty is a highly subjective task, we consider that certain features are important to please users. In this paper, we introduce an adaptive model to learn aesthetic judgements in the task of interactive evolutionary art. Following previous work, we explore a collection of aesthetic measurements based on aesthetic principles. We then reduce them to a relevant subset by feature selection, and build the model by learning the features extracted from previous interactions. To apply a more accurate model, multi-layer perceptron and C4.5 decision tree classifiers are compared. In order to test the efficacy of the approach, an evolutionary art system is built by adopting this model, which analyzes the user’s aesthetic judgements and approximates their implicit aesthetic intentions in the subsequent generations. We first tested these aesthetic measurements on different artworks from our selected artists. Then, a series of experiments were performed by a group of users to validate the adaptive learning model. The study reveals that different features are useful for identifying different patterns, but not all are relevant for the description of artists’ styles. Our results show that the use of the learning model in evolutionary art systems is sound and promising for predicting users’ preferences.

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References

  1. 1.

    E. Acebo, S. Mateu, Benford’s law for natural and synthetic images, in Computational Aesthetics, ed. by L. Neumann, M. Sbert, B. Gooch, W. Purgathofer (Eurographics Association, Aire-la-Ville, 2005) pp. 169–176

    Google Scholar 

  2. 2.

    S. Baluja, D. Pomerleau, T. Jochem, Towards automated artificial evolution for computer-generated images. Connect. Sci. 6(2 and 3), 325–354 (1994)

    Article  Google Scholar 

  3. 3.

    M. Bense, Einführung in die informationstheoretische asthetik. Graundlegung und Anwendung in der Texttheorie (Introduction to the Information-theoretical Aesthetics. Foundation and Application to the Text Theory) (1969)

  4. 4.

    P.J. Bentley (ed.), Evolutionary Design by Computers. (Academic Press, London, 1999)

    MATH  Google Scholar 

  5. 5.

    P.J. Bentley, D.W. Corne, Creative Evolutionary Systems. (Academic Press, London, 2002)

    Google Scholar 

  6. 6.

    S. Bergen, J.R. Brian, Aesthetic 3d model evolution, in EvoMUSART, pp. 11–22 (2012)

  7. 7.

    G.D. Birkhoff, Aesthetic Measure. (Harvard University Press, Cambridge, 1933)

    MATH  Google Scholar 

  8. 8.

    J. Clune, H. Lipson, Evolving 3d objects with a generative encoding inspired by developmental biology. SIGEVOlution 5(4), 2–12 (2011)

    Article  Google Scholar 

  9. 9.

    D. Cohen-Or, O. Sorkine, R. Gal, T. Leyvand, Y. Xu, Color harmonization, in ACM Transactions on Graphics (TOG), pp. 624–630 (2006)

  10. 10.

    R. Dawkins, The Blind Watchmaker. (Penguin Books, London, 1986)

    Google Scholar 

  11. 11.

    S. DiPaola, L. Gabora, Incorporating characteristics of human creativity into an evolutionary art algorithm. Gene. Program. Evol. Mach. 10(2), 97–110 (2009)

    Article  Google Scholar 

  12. 12.

    A. Ekårt, D. Sharma, S. Chalakov, Modelling human preference in evolutionary art, in EvoApplications (2), vol. 6625, ed. by C. Di Chio, A. Brabazon, G.A. Di Caro, R. Drechsler, M. Ebner, M. Farooq, J. Grahl, G. Greenfield, C. Prins, J. Romero, G. Squillero, E. Tarantino, A.G.B. Tettamanzi, N. Urquhart, A.S. Uyar (Springer, Berlin, 2011), pp. 303–312

  13. 13.

    J. Geller, Data mining: practical machine learning tools and techniques with java implementations. SIGMOD Record 31(1), 77 (2002)

    Google Scholar 

  14. 14.

    G. Greenfield, On the origins of the term “computational aesthetics”, in Computational Aesthetics, ed. by L. Neumann, M. Sbert, B. Gooch, W. Purgathofer (Eurographics Association, Aire-la-Ville, 2005), pp. 9–12

  15. 15.

    D. Harwood, T. Ojala, M. Pietikäinen, S. Kelman, L. Davis, Cartr-678-texture classification by center-symmetric auto-correlation, using kullback discrimination of distributions. Technical report, Computer Vision Labratory, Center for Automation Research (University of Maryland, College Park, Maryland, 1993)

  16. 16.

    E. den Heijer, A.E. Eiben, Using aesthetic measures to evolve art, in IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)

  17. 17.

    E. den Heijer, A.E. Eiben, Evolving art using multiple aesthetic measures, in EvoApplications (2), vol. 6625, ed. by C. Di Chio, A. Brabazon, G.A. Di Caro, R. Drechsler, M. Ebner, M. Farooq, J. Grahl, G. Greenfield, C. Prins, J. Romero, G. Squillero, E. Tarantino, A.G.B. Tettamanzi, N. Urquhart, A.S. Uyar (Springer, Berlin, 2011), pp. 234–243

  18. 18.

    F. Hoenig, Defining computational aesthetics, in Computational Aesthetics ed. by L. Neumann, M.S. Casasayas, B. Gooch, W. Purgathofer, (Eurographics Association, Aire-la-Ville, 2005), pp. 13–18

    Google Scholar 

  19. 19.

    S.G. Hornby, J. Bongard, Learning comparative user models for accelerating human-computer collaborative search, in (EvoMUSART, 2012), pp. 117–128

  20. 20.

    G.C. Johnson, Fitness in evolutionary art and music: what has been used and what could be used? in (EvoMUSART, 2012), pp. 129–140

  21. 21.

    Y. Li, C. Hu, Aesthetic learning in an interactive evolutionary art system, in EvoApplications (2), vol. 6025 (Springer, Berlin, 2010), pp. 301–310

  22. 22.

    A. Liapis, G. Yannakakis, J. Togelius, Adapting models of visual aesthetics for personalized content creation. IEEE Transactions on Computational Intelligence and AI in Games Special Issue on Computational Aesthetics in Games (to appear) (2012)

  23. 23.

    L. Liu, R. Chen, L. Wolf, D. Cohen-Or, Optimizing photo composition, in Computer Graphics Forum, vol. 29, ed. by T. Akenine-Moeller, M. Zwicker (Wiley Online Library, 2010), pp. 469–478

  24. 24.

    E. Lutton, Evolution of fractal shapes for artists and designers. Int. J. Artif. Intell. Tools 15(4), 651–672 (2006)

    Article  Google Scholar 

  25. 25.

    P. Machado, A. Cardoso, Computing aesthetics, in Proceedings of XIVth Brazilian Symposium on Artificial Intelligence (SBIA ’98) (Springer 1998), pp. 219–229

  26. 26.

    P. Machado, A. Cardoso, All the truth about NEvAr. Appl. Intell. 16(2), 101–118 (2002)

    MATH  Article  Google Scholar 

  27. 27.

    P. Machado, J. Romero, B. Manaris, Experiments in computational aesthetics. Art Artif. Evol. (2), 381–415 (2008)

  28. 28.

    K. Mainzer, Symmetry and Complexity: The Spirit and Beauty of Nonlinear Science, vol. 51 (World Scientific Publishing Company Incorporated, Singapore, 2005)

    Google Scholar 

  29. 29.

    B. Manaris, P. Roos, P. Machado, D. Krehbiel, L. Pellicoro, J. Romero, A corpus-based hybrid approach to music analysis and composition, in Proceedings of the National Conference on Artificial Intelligence, vol. 22 (Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 2007), p. 839

  30. 30.

    K. Matkovic, L. Neumann, T. Psik, W. Purgathofer, Global contrast factor—a new approach to image contrast, in Computational Aesthetics, ed. by L. Neumann, M. Sbert, B. Gooch, W. Purgathofer (Eurographics Association, Aire-la-Ville, 2005), pp. 159–167

  31. 31.

    T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)

    Article  Google Scholar 

  32. 32.

    G. Papadopoulos, G. Wiggins, AI methods for algorithmic composition: a survey, a critical view and future prospects. in AISB Symposium on Musical Creativity (Edinburgh, UK, 1999), pp. 110–117

  33. 33.

    P.M. Todd, G.M. Werner, Frankensteinian approaches to evolutionary music composition. in Musical Networks: Parallel Distributed Perception and Performance, ed. by N. Griffith, P.M. Todd, (MIT Press, 1999), pp. 313–340. http://www-abc.mpibberlin.mpg.de/users/ptodd/publications/99evmus/99evmus.pdf. Accesed 27 Apr 2005

  34. 34.

    R. Poli, S. Cagnoni, Genetic programming with user-driven selection: experiments on the evolution of algorithms for image enhancement, in Genetic Programming 1997: Proceedings of the Second Annual Conference (Morgan Kaufmann, Stanford University, CA, USA, 1997), pp. 269–277

  35. 35.

    R. Quinlan, C4.5: Programs for Machine Learning. (Morgan Kaufmann Publishers, San Mateo, 1993)

    Google Scholar 

  36. 36.

    J. Rigau, M. Feixas, M. Sbert, Informational dialogue with Van Gogh’s paintings, in Computational Aesthetics, ed. by D.W. Cunningham, V. Interrante, P. Brown, J. McCormack (Eurographics Association, Aire-la-Ville, 2008), pp. 115–122

  37. 37.

    B.J. Ross, W. Ralph, H. Zong, Evolutionary image synthesis using a model of aesthetics, in Proceedings of the 2006 IEEE Congress on Evolutionary Computation ed. by G.G. Yen, L. Wang, P. Bonissone, S.M. Lucas (IEEE Press, Vancouver, 2006) pp. 3832–3839

    Google Scholar 

  38. 38.

    B.J. Ross, H. Zhu, Procedural texture evolution using multiobjective optimization. New Gener. Comput. 22(3), 271–293 (2004)

    MATH  Article  Google Scholar 

  39. 39.

    D. Rumelhart, G. Hintont, R. Williams, Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)

    Article  Google Scholar 

  40. 40.

    J. Secretan, N. Beato, D. D’Ambrosio, A. Rodriguez, A. Campbell, J. Folsom-Kovarik, K. Stanley, Picbreeder: A case study in collaborative evolutionary exploration of design space. Evol. Comput. 19(3), 373–403 (2011)

    Article  Google Scholar 

  41. 41.

    A. Serag, S. Ono, S. Nakayama, Using interactive evolutionary computation to generate creative building designs. Artif. Life Robot. 13(1), 246–250 (2008)

    Article  Google Scholar 

  42. 42.

    C. Shannon, Prediction and entropy of printed english. Bell Syst. Tech. J. 30, 50–64 (1951)

    MATH  Google Scholar 

  43. 43.

    K. Sims, Artificial Evolution for Computer Graphics, vol. 25. ACM (1991)

  44. 44.

    M. Stricker, M. Orengo, Similarity of color images, in Proceedings of SPIE Storage and Retrieval for Image and Video Databases, vol. 2420 (San Diego, CA, 1995), pp. 4

  45. 45.

    N. Svangard, P. Nordin, Automated aesthetic selection of evolutionary art by distance based classification of genomes and phenomes using the universal similarity metric, in Applications of Evolutionary Computing, EvoWorkshops 2004: (EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC 2004), pp. 447–456

  46. 46.

    H. Takagi, Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 89(9), 1275–1296 (2001). doi:10.1109/5.949485

    Article  Google Scholar 

  47. 47.

    H. Takagi, M. Ohsaki, Interactive evolutionary computation-based hearing aid fitting. Evol. Comput. IEEE Trans. 11(3), 414–427 (2007)

    Article  Google Scholar 

  48. 48.

    S. Wang, X. Wang, H. Takagi, User fatigue reduction by an absolute rating data-trained predictor in IEC, in Evolutionary Computation, 2006. CEC 2006. IEEE Congress on (2006), pp. 2195–2200

  49. 49.

    S. Wannarumon, L. J.Bohez, K. Annanon, Aesthetic evolutionary algorithm for fractal-based user-centered jewelry design. AI EDAM 22(1), 19–39 (2008)

    Google Scholar 

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Acknowledgments

The authors would like to thank the editor and the reviewers for their helpful insights on improving the manuscript and Penousal Machado and Juan Romero for their valuable comments. This work is supported by China Postdoctoral Science Foundation (No. 20110490296), "the Fundamental Research Funds for the Central Universities" (No. FRF-TP-12-079A), the 2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science (No. Z121101002812005), National Program on Key Basic Research Project (973 Program) (No. 2013CB329606) and key Science–Technology Plan of the National "Twelfth Five-Year-Plan" of China under Grant (No. 2011BAK08B04).

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Correspondence to Yang Li.

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Li, Y., Hu, C., Minku, L.L. et al. Learning aesthetic judgements in evolutionary art systems. Genet Program Evolvable Mach 14, 315–337 (2013). https://doi.org/10.1007/s10710-013-9188-7

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Keywords

  • Evolutionary art
  • Interactive evolutionary computation
  • Image complexity
  • Fractal compression