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Learning personalized exploration in evolutionary design using aesthetic descriptors

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Abstract

We describe an aesthetic learning approach to one of the most challenging problems in interactive evolutionary design: modeling user preference for lessening the burden placed on users in hundreds of loops. In the approach, two aesthetic descriptors, high-level and low-level descriptors, are proposed based on pixel distribution and aesthetic criteria respectively. Starting with a collection of evaluated images, we apply both descriptors to the images, and then use decision tree learning algorithm to obtain the computational learning model. The model is adopted to automatically guide the subsequent generations. Classification and evolutionary results are shown in our experiments to evaluate the learning model and compare the two descriptors’ learning ability in the evolutionary runs. The reported results indicate that high-level descriptors are more appropriate in approximating user’s implicit aesthetic intentions for solving the problem considered.

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References

  1. Acebo, E., Mateu, S.: Benford’s law for natural and synthetic images. In: Neumann, L., Sbert, M., Gooch, B., Purgathofer, W. (eds.) Computational Aesthetics, pp. 169–176. Eurographics Association (2005)

  2. Arciszewski, T., Cornell, J.: Bio-inspiration: learning creative design principia. In: Intelligent Computing in Engineering and Architecture, pp. 32–53. Springer, New York (2006)

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

    Article  Google Scholar 

  4. Bense, M.: Einführung in die informationstheoretische asthetik (1969)

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

    MATH  Google Scholar 

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

    Book  MATH  Google Scholar 

  7. Chandrasegaran, S.K., Ramani, K., Sriram, R.D., Horváth, I., Bernard, A., Harik, R.F., Gao, W.: The evolution, challenges, and future of knowledge representation in product design systems. Comput-Aided Des. 45(2), 204–228 (2013)

    Article  Google Scholar 

  8. Chen, W., Simpson, T.W., Allen, J.K., Mistree, F.: Satisfying ranged sets of design requirements using design capability indices as metrics. Eng. Optim. 31(5), 615–619 (1999)

    Article  Google Scholar 

  9. Ciesielski, V., Barile, P., Trist, K.: Finding image features associated with high aesthetic value by machine learning. In: Machado, P., McDermott, J., Carballal, A. (eds.) Proceedings of the second International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2013. Lecture Notes in Computer Science, vol. 7834, pp. 47–58. Springer, Vienna (2013)

    Google Scholar 

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

    Article  Google Scholar 

  11. Cohen-Or, D., Sorkine, O., Gal, R., Leyvand, T., Xu, Y.Q.: Color Harmonization. In: Proceedings of ACM SIGGRAPH, ACM Transactions on Graphics, pp. 624–630 (2006)

  12. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Article  Google Scholar 

  13. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Studying aesthetics in photographic images using a computational approach. In: Computer Vision—ECCV’06, pp. 288–301. Springer, New York (2006)

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

  15. den Heijer, E., Eiben, A.E.: Evolving art using multiple aesthetic measures. In: EvoApplications (2), vol. 6625, pp. 234–243. Springer (2011)

  16. Devanathan, S., Ramani, K.: Creating polytope representations of design spaces for visual exploration using consistency techniques. J. Mech. Des. 132(8), 081,011 (2010)

    Article  Google Scholar 

  17. Ekårt, A., Sharma, D., Chalakov, S.: Modelling human preference in evolutionary art. In: Applications of Evolutionary Computation, pp. 303–312. Springer (2011)

  18. Fischer, X., Coutellier, D.: Research in Interactive Design: Proceedings of Virtual Concept 2005. Springer Science & Business Media, New York (2006)

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

    Google Scholar 

  20. Goodman, E., Stolterman, E., Wakkary, R.: Understanding interaction design practices. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1061–1070. ACM, New York (2011)

  21. Greenfield, G.: On the origins of the term “computational aesthetics”. In: Computational Aesthetics, pp. 9–12. Eurographics Association (2005)

  22. Guo-sheng, H., Dun-Wei, G., Yong-Qing, H.: Interactive genetic algorithms based on estimation of user’s most satisfactory individuals. In: Sixth International Conference on Intelligent Systems Design and Applications, 2006 (ISDA’06), vol. 3, pp. 132–137. IEEE (2006)

  23. Harwood, D., Ojala, T., Pietikäinen, M., Kelman, S., Davis, L.: 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 (1993)

  24. Helms, M., Vattam, S.S., Goel, A.K.: Biologically inspired design: process and products. Des. Stud. 30(5), 606–622 (2009)

    Article  Google Scholar 

  25. Hoenig, F.: Defining computational aesthetics. In: Neumann, L., Casasayas, M.S., Gooch, B., Purgathofer, W. (eds.) Computational Aesthetics, pp. 13–18. Eurographics Association (2005)

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

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

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

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

  30. Machado, P., Romero, J., Manaris, B.: The art of artificial evolution: a handbook on evolutionary art and music. Exp. Comput. Aesthet. Iterative Approach Stylist. Chang. Evol. Art 15(2), 381–415 (2009)

    Google Scholar 

  31. Mainzer, K.: Symmetry and complexity: the spirit and beauty of nonlinear science (2005)

  32. Mak, T.W., Shu, L.H.: Abstraction of biological analogies for design. CIRP Ann. Manuf Technol. 53(1), 117–120 (2004)

    Article  Google Scholar 

  33. Manaris, B., Roos, P., Machado, P., Krehbiel, D., Pellicoro, L., Romero, J.: A corpus-based hybrid approach to music analysis and composition (2008)

  34. Matkovic, K., Neumann, L., Neumann, A., Psik, T., Purgathofer, W.: Global contrast factor–a new approach to image contrast. Comput. Aesthet. 2005, 159–168 (2005)

    Google Scholar 

  35. Michopoulos, J.G., Farhat, C., Fish, J.: Modeling and simulation of multiphysics systems. J. Comput. Inf. Sci. Eng. 5(3), 198–213 (2005)

    Article  Google Scholar 

  36. Mobach, M.: Interactive Design for Facility Innovation, pp. 1 – 7. Springer, New York (2010)

  37. Nass, C., Klöckner, K., Diefenbach, S., Hassenzahl, M.: Designi: a workbench for supporting interaction design. In: Proceedings of the 6th Nordic Conference on Human–Computer Interaction: Extending Boundaries, pp. 747–750. ACM, New York (2010)

  38. Ojala, T., Pietikäinen, M., Harwood, D.: A Comparative Study of Texture Measures with Classification Based on Featured Distributions, pp. 51–59 (1996)

  39. Ou, L.-C., Ronnier Luo, M., Sun, P.-L., Hu, N.-C., Chen, H.-S., Guan, S.-S., Woodcock, A., Caivano, J.L., Huertas, R., Treméau, A., et al.: A cross-cultural comparison of colour emotion for two-colour combinations. Color Res. Appl. 37(1), 23–43 (2012)

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

  41. Park, S., Nam, T.J.: Product-personification method for generating interaction ideas. Int. J. Interact. Des. Manuf. (IJIDeM) 9(2), 97–105 (2015)

    Article  Google Scholar 

  42. Peter, M.T., Gregory, M.W.: Frankensteinian approaches to evolutionary music composition. In: Musical Networks: Parallel Distributed Perception and Performance, pp. 313–340 (1999)

  43. Pfeifer, R., Lungarella, M., Iida, F.: Self-organization, embodiment, and biologically inspired robotics. Science 318(5853), 1088–1093 (2007)

  44. Preece, J., Sharp, H., Rogers, Y.: Interaction Design-Beyond Human–Computer Interaction. Wiley, New York (2015)

    Google Scholar 

  45. Quinlan, J.R.: C4. 5: programs for machine learning. Elsevier (2014)

  46. Renjie, L.L., Lior, C., Cohen-or, W.D., Liu, L., Chen, R., Wolf, L., Cohen-or, D.: Optimizing photo composition (2010)

  47. Rigau, J., Feixas, M., Sbert, M.: Informational dialogue with van gogh’s paintings. In: Proceedings of the Fourth Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging, pp. 115–122. Eurographics Association (2008)

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

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

    Google Scholar 

  50. Saffer, D.: Designing for Interaction: Creating Smart Applications and Clever Devices. Peachpit Press, Berkeley (2006)

    Google Scholar 

  51. Schmidhuber, J.: Low-complexity art. Leonardo J. Int. Soc. Arts Sci. Technol. 30(2), 97–103 (1997)

    Google Scholar 

  52. Secretan, J., Beato, N., D’Ambrosio, D.B., Rodriguez, A., Campbell, A., Folsom-Kovarik, J.T., Stanley, K.O.: Picbreeder: A case study in collaborative evolutionary exploration of design space. Evol. Comput. 19(3), 373–403 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  55. Sims, K.: Artificial Evolution for Computer Graphics, pp. 319–328. ACM, New York (1991)

  56. Solli, M., Lenz, R., Lenz, R.: Color based bags-of-emotions. In: CAIP, pp. 573–580 (2009)

  57. Srikanth, D., Karthik, R., et al.: Combining constraint satisfaction and non-linear optimization to enable configuration driven design. In: Guidelines for a Decision Support Method Adapted to NPD Processes (2007)

  58. Stricker, M., Orengo, M.: Similarity of color images. In: IS&T/SPIE’s Symposium on Electronic Imaging: Science & Technology, pp. 381–392. International Society for Optics and Photonics (1995)

  59. Takagi, H.: 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 

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

  61. Unemi, T.: Sbart4 for an automatic evolutionary art. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)

  62. Vakili, V., Chiu, I., Shu, L., McAdams, D., Stone, R.: Including functional models of biological phenomena as design stimuli. In: ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 103–113. American Society of Mechanical Engineers (2007)

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Acknowledgments

This work was supported by Fundamental Research Funds for the Central Universities (FRF-TP-15-029A2), the R D Infrastructure and Facility Development Program of China (Grant No. 2005DKA32800), the 2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science (Grant No. Z121101002812005), the Key Science-Technology Plan of the National “Twelfth Five-Year-Plan” Project of China (Grant No.2011BAK08B04), and the National Key Basic Research and Development Program (973 Program) of China (Grant No. 2013CB329606).

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  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, No. 30 Xueyuan Road, HaiDian District, Beijing, 100083, People’s Republic of China

    Yang Li & Changjun Hu

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  1. Yang Li
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Li, Y., Hu, C. Learning personalized exploration in evolutionary design using aesthetic descriptors. Int J Interact Des Manuf 11, 489–501 (2017). https://doi.org/10.1007/s12008-015-0294-z

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