Correlation Between Human Aesthetic Judgement and Spatial Complexity Measure
Inhaltsverzeichnis
Referenz
Mohammad Ali Javaheri Javid, Tim Blackwell, Robert Zimmer, Mohammad Majid al-Rifaie: Correlation Between Human Aesthetic Judgement and Spatial Complexity Measure. In: EvoMUSART 2016, 79-91.
DOI
http://dx.doi.org/10.1007/978-3-319-16498-4_6
Abstract
The quantitative evaluation of order and complexity conforming with human intuitive perception has been at the core of computational notions of aesthetics. Informational theories of aesthetics have taken advantage of entropy in measuring order and complexity of stimuli in relation to their aesthetic value. However entropy fails to discriminate structurally different patterns in a 2D plane. This paper investigates a computational measure of complexity, which is then compared to a results from a previous experimental study on human aesthetic perception in the visual domain. The model is based on the information gain from specifying the spacial distribution of pixels and their uniformity and non-uniformity in an image. The results of the experiments demonstrate the presence of correlations between a spatial complexity measure and the way in which humans are believed to aesthetically appreciate asymmetry. However the experiments failed to provide a significant correlation between the measure and aesthetic judgements of symmetrical images.
Extended Abstract
Bibtex
@incollection{Javid2016,
year={2016},
isbn={978-3-319-31007-7},
booktitle={Evolutionary and Biologically Inspired Music, Sound, Art and Design},
volume={9596},
series={Lecture Notes in Computer Science},
editor={Johnson, Colin and Ciesielski, Vic and Correia, João and Machado, Penousal},
doi={http://dx.doi.org/10.1007/978-3-319-16498-4_6},
title={Correlation Between Human Aesthetic Judgement and Spatial Complexity Measure},
url={http://link.springer.com/chapter/10.1007/978-3-319-31008-4_6 http://de.evo-art.org/index.php?title=Correlation_Between_Human_Aesthetic_Judgement_and_Spatial_Complexity_Measure },
publisher={Springer International Publishing},
keywords={Human aesthetic judgements, Spatial complexity, Information theory, Symmetry, Complexity},
author={Javid, Mohammad Ali Javaheri and Blackwell, Tim and Zimmer, Robert and al-Rifaie, Mohammad Majid},
pages={79-91},
language={English}
}
Used References
1. Andrienko, Y.A., Brilliantov, N.V., Kurths, J.: Complexity of two-dimensional patterns. Eur. Phys. J. B 15(3), 539–546 (2000 http://dx.doi.org/10.1007/s100510051157
2. Arnheim, R.: Art and Visual Perception: A Psychology of the Creative Eye. Univ of California Press, Berkeley (1954)
3. Arnheim, R.: Towards a Psychology of Art/entropy and Art an Essay on Disorder and Order. The Regents of the University of California (1966)
4. Arnheim, R.: Visual Thinking. Univ of California Press, Berkeley (1969)
5. Bates, J.E., Shepard, H.K.: Measuring complexity using information fluctuation. Phys. Lett. A 172(6), 416–425 (1993) http://dx.doi.org/10.1016/0375-9601(93)90232-O
6. Bense, M., Nee, G.: Computer grafik. In: Bense, M., Walther, E. (eds.) Edition Rot, vol. 19. Walther, Stuttgart (1965)
7. Bense, M.: Aestetica: Programmierung des Schönen, allgemeine Texttheorie und Textästhetik [Aesthetica : Programming of beauty, general text theory and aesthetics]. Agis-Verlag (1960)
8. Bense, M.: Kleine abstrakte ästhetik [small abstract aesthetics]. In: Walther, E. (ed.) Edition Rot, vol. 38 (1969)
9. Birkhoff, G.: Aesthetic Measure. Harvard University Press, Cambridge (1933) http://dx.doi.org/10.4159/harvard.9780674734470
10. 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.) EvoMUSART 2013. LNCS, vol. 7834, pp. 47–58. Springer, Heidelberg (2013) http://dx.doi.org/10.1007/978-3-642-12242-2_32
11. Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Series in Telecommunications and Signal Processing. Wiley-Interscience, New York (2006)MATH
12. Dawkins, R.: The Blind Watchmaker. W. W. Norton, New York (1986)
13. den Heijer, E., Eiben, A.E.: Comparing aesthetic measures for evolutionary art. In: Chio, C., Brabazon, A., 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) http://dx.doi.org/10.1007/978-3-642-12242-2_32
14. Eysenck, H.J.: An experimental study of aesthetic preference for polygonal figures. J. Gen. Psychol. 79(1), 3–17 (1968) http://dx.doi.org/10.1080/00221309.1968.9710447
15. Eysenck, H.J.: The empirical determination of an aesthetic formula. Psychol. Rev. 48(1), 83 (1941) http://dx.doi.org/10.1037/h0062483
16. Eysenck, H.J.: The experimental study of the ‘good gestalt’ –a new approach. Psychol. Rev. 49(4), 344 (1942) http://dx.doi.org/10.1037/h0057013
17. Franke, H.W.: A cybernetic approach to aesthetics. Leonardo 10(3), 203–206 (1977) http://dx.doi.org/10.2307/1573423
18. Galanter, P.: Computational aesthetic evaluation: past and future. In: McCormack, J., d’IInverno, M. (eds.) Computer and Creativity, pp. 255–293. Springer, Heidelberg (2012) http://dx.doi.org/10.1007/978-3-642-31727-9_10
19. den Heijer, E.: Autonomous Evolutionary Art, Ph.D. thesis. Vrije Universiteit, Amsterdam (2013)
20. Jacobsen, T.: Beauty and the brain: culture, history and individual differences in aesthetic appreciation. J. Anat. 216(2), 184–191 (2010) http://dx.doi.org/10.1111/j.1469-7580.2009.01164.x
21. Jacobsen, T., Hofel, L.: Aesthetic judgments of novel graphic patterns: analyses of individual judgments. Percept. Mot. Skills 95(3), 755–766 (2002) http://dx.doi.org/10.2466/pms.2002.95.3.755
22. Javaheri Javid, M.A., Blackwell, T., Zimmer, R., Al-Rifaie, M.M.: Spatial complexity measure for characterising cellular automata generated 2D patterns. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds.) EPIA 2015. LNCS, vol. 9273, pp. 201–212. Springer, Heidelberg (2015)
23. Javid, M.A.J., al-Rifaie, M.M., Zimmer, R.: An informational model for cellular automata aesthetic measure. In: AISB Symposium on Computational Creativity. University of Kent, Canterbury, UK (2015)
24. Latham, W.H., Todd, S.: Computer sculpture. IBM Syst. J. 28(4), 682–688 (1989) http://dx.doi.org/10.1147/sj.284.0682
25. Li, M.: An introduction to Kolmogorov complexity and its applications. Springer, New York (1997) http://dx.doi.org/10.1007/978-1-4757-2606-0
26. Machado, P., Cardoso, A.: Computing aesthetics. In: de Oliveira, F.M. (ed.) SBIA 1998. LNCS (LNAI), vol. 1515, pp. 219–228. Springer, Heidelberg (1998) http://dx.doi.org/10.1007/10692710_23
27. 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 (2008) http://dx.doi.org/10.1007/978-3-540-72877-1_18
28. 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) http://dx.doi.org/10.1007/978-3-540-32003-6_43
29. McCormack, J.: Facing the future: evolutionary possibilities for human-machine creativity. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution, pp. 417–451. Springer, Heidleberg (2008) http://dx.doi.org/10.1007/978-3-540-72877-1_19
30. Moles, A.: Information Theory and Esthetic Perception. Illinois Press, Urbana (1968). Trans. JE Cohen. U
31. Nake, F.: Information aesthetics: an heroic experiment. J. Math. Arts 6(2–3), 65–75 (2012) http://dx.doi.org/10.1080/17513472.2012.679458
32. Noll, A.M.: The digital computer as a creative medium. IEEE Spectr. 4(10), 89–95 (1967) http://dx.doi.org/10.1109/MSPEC.1967.5217127
33. Rigau, J., Feixas, M., Sbert, M.: Informational aesthetics measures. IEEE Comput. Graph. Appl. 28(2), 24–34 (2008) http://dx.doi.org/10.1109/MCG.2008.34
34. Rigau, J., Feixas, M., Sbert, M.: Conceptualizing Birkhoff’s aesthetic measureusing shannon entropy and kolmogorov complexity. In: Cunningham, D.W., Meyer, G., Neumann, L. (eds.) Workshop on Computational Aesthetics, pp. 105–112. Eurographics Association, Banff, Alberta, Canada (2007)
35. Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948)
36. Sims, K.: Artificial evolution for computer graphics. Technical Report, TR-185, Thinking Machines Corporation (1991)
37. Staudek, T.: Exact aesthetics, object and scene to message. Ph.D. thesis, Faculty of Informatics, Masaryk University of Brno (2002)
38. Wackerbauer, R., Witt, A., Atmanspacher, H., Kurths, J., Scheingraber, H.: A comparative classification of complexity measures. Chaos, Solitons & Fractals 4(1), 133–173 (1994) http://dx.doi.org/10.1016/0960-0779(94)90023-X
39. Wilson, D.J.: An experimental investigation of Birkhoff’s aesthetic measure. J. Abnorm. Soc. Psychol. 34(3), 390 (1939) http://dx.doi.org/10.1037/h0059439
40. Zurek, W.H.: Algorithmic randomness and physical entropy. Phys. Rev. A 40(8), 4731 (1989) http://dx.doi.org/10.1103/PhysRevA.40.4731
Links
Full Text
