Computing Aesthetics with Image Judgement Systems

  • Juan Romero
  • Penousal Machado
  • Adrian Carballal
  • João Correia

Abstract

The ability of human or artificial agents to evaluate their works, as well as the works of others, is an important aspect of creative behaviour, possibly even a requirement. In artistic fields such as visual arts and music, this evaluation capacity relies, at least partially, on aesthetic judgement. This chapter analyses issues regarding the development of computational systems that perform aesthetic judgements focusing on their validation. We present several alternatives, as follows: the use of psychological tests related to aesthetic judgement; the testing of these systems in style recognition tasks; and the assessment of the system’s ability to predict the users’ valuations or the popularity of a given work. An adaptive system is presented and its performance assessed using the above-mentioned validation methodologies.

Keywords

Artificial Neural Network Psychological Test Content Base Image Retrieval Evolutionary Engine Aesthetic Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments, suggestions and criticisms. This research is partially funded by: the Spanish Ministry for Science and Technology, research project TIN2008-06562/TIN; the Portuguese Foundation for Science and Technology, research project PTDC/EIA-EIA/115667/2009; Xunta de Galicia, research project XUGA-PGIDIT10TIC105008-PR.

References

  1. Arnheim, R. (1956). Art and visual perception, a psychology of the creative eye. London: Faber and Faber. Google Scholar
  2. Arnheim, R. (1966). Towards a psychology of art/entropy and art—an essay on disorder and order. The Regents of the University of California. Google Scholar
  3. Arnheim, R. (1969). Visual thinking. Berkeley: University of California Press. Google Scholar
  4. Athitsos, V., Swain, M. J., & Frankel, C. (1997). Distinguishing photographs and graphics on the world wide web. In Proceedings of the 1997 workshop on content-based access of image and video libraries (CBAIVL ’97), CAIVL ’97 (pp. 10–17). Washington: IEEE Computer Society. http://portal.acm.org/citation.cfm?id=523204.791698. CrossRefGoogle Scholar
  5. Baluja, S., Pomerlau, D., & Todd, J. (1994). Towards automated artificial evolution for computer-generated images. Connection Science, 6(2), 325–354. CrossRefGoogle Scholar
  6. Boden, M. A. (1990). The creative mind: myths and mechanisms. New York: Basic Books. Google Scholar
  7. Burt, C. (1933). The psychology of art. In How the mind works. London: Allen and Unwin. Google Scholar
  8. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698. CrossRefGoogle Scholar
  9. Chamorro-Premuzic, T., & Furnham, A. (2004). Art judgement: a measure related to both personality and intelligence? Imagination, Cognition and Personality, 24, 3–25. CrossRefGoogle Scholar
  10. Cope, D. (1992). On the algorithmic representation of musical style. In O. Laske (Ed.), Understanding music with AI: perspectives on music cognition (pp. 354–363). Cambridge: MIT Press. Google Scholar
  11. Cutzu, F., Hammoud, R. I., & Leykin, A. (2003). Estimating the photorealism of images: distinguishing paintings from photographs. In CVPR (2) (pp. 305–312). Washington: IEEE Computer Society. Google Scholar
  12. Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2006). Studying aesthetics in photographic images using a computational approach. In Lecture notes in computer science. Computer vision—ECCV 2006, 9th European conference on computer vision, part III, Graz, Austria (pp. 288–301). Berlin: Springer. Google Scholar
  13. Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008). Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys, 40, 5:1–5:60. http://doi.acm.org/10.1145/1348246.1348248. CrossRefGoogle Scholar
  14. Dorin, A., & Korb, K. B. (2009). Improbable creativity. In M. Boden, M. D’Inverno, & J. McCormack (Eds.), Dagstuhl seminar proceedings: Vol. 09291. Computational creativity: an interdisciplinary approach, Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany. http://drops.dagstuhl.de/opus/volltexte/2009/2214. Google Scholar
  15. Eysenck, H. (1969). Factor analytic study of the Maitland Graves Design Judgement Test. Perceptual and Motor Skills, 24, 13–14. Google Scholar
  16. Eysenck, H. J. (1983). A new measure of ‘good taste’ in visual art. Leonardo, Special Issue: Psychology and the Arts, 16(3), 229–231. http://www.jstor.org/stable/1574921. Google Scholar
  17. Eysenck, H. J., & Castle, M. (1971). Comparative study of artists and nonartists on the Maitland Graves Design Judgment Test. Journal of Applied Psychology, 55(4), 389–392. CrossRefGoogle Scholar
  18. Eysenck, H. J., Götz, K. O., Long, H. Y., Nias, D. K. B., & Ross, M. (1984). A new visual aesthetic sensitivity test—IV. Cross-cultural comparisons between a Chinese sample from Singapore and an English sample. Personality and Individual Differences, 5(5), 599–600. http://www.sciencedirect.com/science/article/B6V9F-45WYSPS-1M/2/1b43c2e7ad32ef89313f193d3358b441. CrossRefGoogle Scholar
  19. Field, D. J., Hayes, A., & Hess, R. F. (2000). The roles of polarity and symmetry in the perceptual grouping of contour fragments. Spatial Vision, 13(1), 51–66. CrossRefGoogle Scholar
  20. Fisher, Y. (Ed.) (1995). Fractal image compression: theory and application. London: Springer. Google Scholar
  21. Frois, J., & Eysenck, H. J. (1995). The visual aesthetic sensitivity test applied to Portuguese children and fine arts students. Creativity Research Journal, 8(3), 277–284. http://www.leaonline.com/doi/abs/10.1207/s15326934crj0803_6. CrossRefGoogle Scholar
  22. Furnham, A., & Walker, J. (2001). The influence of personality traits, previous experience of art, and demographic variables on artistic preference. Personality and Individual Differences, 31(6), 997–1017. http://www.sciencedirect.com/science/article/B6V9F-440BD9B-J/2/c107a7e1db8199da25fb754780a7d220. CrossRefGoogle Scholar
  23. Götz, K. (1985). VAST: visual aesthetic sensitivity test. Dusseldorf: Concept Verlag. Google Scholar
  24. Götz, K. O., & Götz, K. (1974). The Maitland Graves Design Judgement Test judged by 22 experts. Perceptual and Motor Skills, 39, 261–262. CrossRefGoogle Scholar
  25. Graves, M. (1946). Design judgement test. New York: The Psychological Corporation. Google Scholar
  26. Graves, M. (1948). Design judgement test, manual. New York: The Psychological Corporation. Google Scholar
  27. Graves, M. (1951). The art of color and design. New York: McGraw-Hill. Google Scholar
  28. Itten, J. (1973). The art of color: the subjective experience and objective rationale of color. New York: Wiley. Google Scholar
  29. Iwawaki, S., Eysenck, H. J., & Götz, K. O. (1979). A new visual aesthetic sensitivity test (vast): II. Cross cultural comparison between England and Japan. Perceptual and Motor Skills, 49(3), 859–862. http://www.biomedsearch.com/nih/new-Visual-Aesthetic-Sensitivity-Test/530787.html. CrossRefGoogle Scholar
  30. Ke, Y., Tang, X., & Jing, F. (2006). The design of high-level features for photo quality assessment. Computer Vision and Pattern Recognition, IEEE Computer Society Conference, 1, 419–426. Google Scholar
  31. Kowaliw, T., Dorin, A., & McCormack, J. (2009). An empirical exploration of a definition of creative novelty for generative art. In K. B. Korb, M. Randall & T. Hendtlass (Eds.), Lecture notes in computer science: Vol. 5865. ACAL (pp. 1–10). Berlin: Springer. Google Scholar
  32. Luo, Y., & Tang, X. (2008). Photo and video quality evaluation: focusing on the subject. In D. A. Forsyth, P. H. S. Torr & A. Zisserman (Eds.), Lecture notes in computer science: Vol. 5304. ECCV (3) (pp. 386–399). Berlin: Springer. Google Scholar
  33. Lyu, S., & Farid, H. (2005). How realistic is photorealistic? IEEE Transactions on Signal Processing, 53(2), 845–850. MathSciNetCrossRefGoogle Scholar
  34. Machado, P., & Cardoso, A. (1998). Computing aesthetics. In F. Oliveira (Ed.), Lecture notes in computer science: Vol. 1515. Proceedings of the XIVth Brazilian symposium on artificial intelligence: advances in artificial intelligence, Porto Alegre, Brazil (pp. 219–229). Berlin: Springer. Google Scholar
  35. Machado, P., & Cardoso, A. (2002). All the truth about NEvAr. Applied Intelligence, Special Issue on Creative Systems, 16(2), 101–119. MATHGoogle Scholar
  36. Machado, P., Romero, J., & Manaris, B. (2007). Experiments in computational aesthetics: an iterative approach to stylistic change in evolutionary art. In J. Romero & P. Machado (Eds.), The art of artificial evolution: a handbook on evolutionary art and music (pp. 381–415). Berlin: Springer. Google Scholar
  37. Machado, P., Romero, J., Manaris, B., Santos, A., & Cardoso, A. (2003). Power to the critics—a framework for the development of artificial art critics. In IJCAI 2003 workshop on creative systems, Acapulco, Mexico. Google Scholar
  38. Machado, P., Romero, J., Santos, A., Cardoso, A., & Manaris, B. (2004). Adaptive critics for evolutionary artists. In R. Günther et al. (Eds.), Lecture notes in computer science: Vol. 3005. Applications of evolutionary computing, EvoWorkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC, Coimbra, Portugal (pp. 435–444). Berlin: Springer. Google Scholar
  39. Manaris, B., Romero, J., Machado, P., Krehbiel, D., Hirzel, T., Pharr, W., & Davis, R. (2005). Zipf’s law, music classification and aesthetics. Computer Music Journal, 29(1), 55–69. CrossRefGoogle Scholar
  40. Manaris, B., Roos, P., Machado, P., Krehbiel, D., Pellicoro, L., & Romero, J. (2007). A corpus-based hybrid approach to music analysis and composition. In Proceedings of the 22nd conference on artificial intelligence (AAAI 07), Vancouver, BC. Google Scholar
  41. Marchenko, Y., Chua, T.-S., & Aristarkhova, I. (2005). Analysis and retrieval of paintings using artistic color concepts. In ICME (pp. 1246–1249). New York: IEEE Press. Google Scholar
  42. Nadal, M. (2007). Complexity and aesthetic preference for diverse visual stimuli. PhD thesis, Departament de Psicologia, Universitat de les Illes Balears. Google Scholar
  43. Neufeld, C., Ross, B., & Ralph, W. (2007). The evolution of artistic filters. In J. Romero & P. Machado (Eds.), The art of artificial evolution. Berlin: Springer. Google Scholar
  44. Rigau, J., Feixas, M., & Sbert, M. (2008). Informational dialogue with Van Gogh’s paintings. In Eurographics symposium on computational aesthetics in graphics, visualization and imaging (pp. 115–122). Google Scholar
  45. Romero, J., Machado, P., Santos, A., & Cardoso, A. (2003). On the development of critics in evolutionary computation artists. In R. Günther et al. (Eds.), Lecture notes in computer science: Vol. 2611. Applications of evolutionary computing, EvoWorkshops 2003: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC, Essex, UK. Berlin: Springer. Google Scholar
  46. Saunders, R. (2001). Curious design agents and artificial creativity—a synthetic approach to the study of creative behaviour. PhD thesis, University of Sydney, Department of Architectural and Design Science Faculty of Architecture, Sydney, Australia. Google Scholar
  47. Savarese, J. M., & Miller, R. (1979). Artistic preferences and cognitive-perceptual style. Studies in Art Education, 20, 41–45. CrossRefGoogle Scholar
  48. Schmidhuber, J. (1997). Low-complexity art. Leonardo, Journal of the International Society for the Arts, Sciences, and Technology, 30(2), 97–103. http://www.jstor.org/stable/1576418. Google Scholar
  49. Schmidhuber, J. (1998). Facial beauty and fractal geometry. http://cogprints.org/690/.
  50. Schmidhuber, J. (2007). Simple algorithmic principles of discovery, subjective beauty, selective attention, curiosity and creativity. In M. Hutter, R. A. Servedio & E. Takimoto (Eds.), Lecture notes in computer science: Vol. 4754. ALT (pp. 32–33). Berlin: Springer. Google Scholar
  51. Sobel, I. (1990). An isotropic 3×3 image gradient operator. In Machine vision for three-dimensional scenes (pp. 376–379). Google Scholar
  52. Spector, L., & Alpern, A. (1994). Criticism, culture, and the automatic generation of artworks. In Proceedings of twelfth national conference on artificial intelligence (pp. 3–8). Seattle/Washington: AAAI Press/MIT Press. Google Scholar
  53. Spehar, B., Clifford, C. W. G., Newell, N., & Taylor, R. P. (2003). Universal aesthetic of fractals. Computers and Graphics, 27(5), 813–820. CrossRefGoogle Scholar
  54. Staudek, T. (2002). Exact aesthetics. Object and scene to message. PhD thesis, Faculty of Informatics, Masaryk University of Brno. Google Scholar
  55. Staudek, T. (2003). Computer-aided aesthetic evaluation of visual patterns. In ISAMA-BRIDGES conference proceedings, Granada, Spain (pp. 143–149). Google Scholar
  56. Svangård, N., & Nordin, P. (2004). Automated aesthetic selection of evolutionary art by distance based classification of genomes and phenomes using the universal similarity metric. In R. Günther et al. (Eds.), Lecture notes in computer science: Vol. 3005. Applications of evolutionary computing, EvoWorkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC, Coimbra, Portugal (pp. 445–454). Berlin: Springer. Google Scholar
  57. Taylor, R. P., Micolich, A. P., & Jonas, D. (1999). Fractal analysis of Pollock’s drip paintings. Nature, 399, 422. CrossRefGoogle Scholar
  58. Teller, A., & Veloso, M. (1996). PADO: a new learning architecture for object recognition. In K. Ikeuchi & M. Veloso (Eds.), Symbolic visual learning (pp. 81–116). London: Oxford University Press. http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/PADO.ps.Z. Google Scholar
  59. Tong, H., Li, M., Zhang, H., He, J., & Zhang, C. (2004). Classification of digital photos taken by photographers or home users. In K. Aizawa, Y. Nakamura & S. Satoh (Eds.), Lecture notes in computer science: Vol. 3331. PCM (1) (pp. 198–205). Berlin: Springer. Google Scholar
  60. Tyler, C. W. (Ed.) (2002). Human symmetry perception and its computational analysis. Hillsdale: Erlbaum. Google Scholar
  61. Uduehi, J. (1995). A cross-cultural assessment of the maitland graves design judgment test using U.S. and Nigerian students. Visual Arts Research, 21(2), 11–18. Google Scholar
  62. Wallraven, C., Cunningham, D. W., & Fleming, R. (2008). Perceptual and computational categories in art. In P. Brown (Ed.), International symposium on computational aesthetics in graphics, visualization, and imaging (pp. 131–138). Aire-la-Ville: Eurographics Association. http://computational-aesthetics.org/2008/. Google Scholar
  63. Wallraven, C., Fleming, R. W., Cunningham, D. W., Rigau, J., Feixas, M., & Sbert, M. (2009). Categorizing art: comparing humans and computers. Computers & Graphics, 33(4), 484–495. CrossRefGoogle Scholar
  64. Wertheimer, M. (1939). Laws of organization in perceptual forms. In W. D. Ellis (Ed.), A source book of gestalt psychology (pp. 71–88). New York: Harcourt Brace. Google Scholar
  65. Wong, L.-K., & Low, K.-L. (2009). Saliency-enhanced image aesthetics class prediction. In ICIP (pp. 997–1000). New York: IEEE Press. Google Scholar
  66. Yan, Y., & Jin, J. S. (2005). Indexing and retrieving oil paintings using style information. In S. Bres & R. Laurini (Eds.), Lecture notes in computer science: Vol. 3736. VISUAL (pp. 143–152). Berlin: Springer. Google Scholar
  67. Zell, A., Mamier, G., Vogt, M., Mache, N., Hübner, R., Döring, S., Herrmann, K.-U., Soyez, T., Schmalzl, M., Sommer, T., et al. (2003). SNNS: Stuttgart neural network simulator user manual, version 4.2 (Technical Report 3/92). University of Stuttgart, Stuttgart. Google Scholar
  68. Zipf, G. K. (1949). Human behaviour and the principle of least effort: an introduction to human ecology. Reading: Addison-Wesley. Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Juan Romero
    • 1
  • Penousal Machado
    • 2
  • Adrian Carballal
    • 1
  • João Correia
    • 2
  1. 1.Faculty of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.Department of Informatics EngineeringUniversity of Coimbra – Polo IICoimbraPortugal

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