Computerized measures of visual complexity - Versionsgeschichte

Gubachelier: /* Used References */

2016-06-15T20:13:23Z

‎Used References

Gubachelier am 15. Juni 2016 um 11:59 Uhr

2016-06-15T11:59:37Z

Gubachelier: Die Seite wurde neu angelegt: „== Referenz == P. Machado, J. Romero, M. Nadal, A. Santos, J. Correia, and A. Carballal: Computerized measures of visual complexity. Acta Psychologica, vo…“

2016-06-15T11:57:48Z

Die Seite wurde neu angelegt: „== Referenz == P. Machado, J. Romero, M. Nadal, A. Santos, J. Correia, and A. Carballal: Computerized measures of visual complexity. Acta Psychologica, vo…“

Neue Seite

== Referenz ==
P. Machado, J. Romero, M. Nadal, A. Santos, J. Correia, and A. Carballal: [[Computerized measures of visual complexity]]. Acta Psychologica, vol. 160, iss. 1, pp. 43-57, 2015.

== DOI ==
http://dx.doi.org/10.1016/j.actpsy.2015.06.005

== Abstract ==
Visual complexity influences people's perception of, preference for, and behaviour toward many classes of objects, from artworks to web pages. The ability to predict people's impression of the complexity of different kinds of visual stimuli holds, therefore, great potential for many domains, basic and applied. Here we use edge detection operations and several image metrics based on image compression error and Zipf's law to estimate the visual complexity of images. The experiments involved 800 images, each previously rated by thirty participants on perceived complexity. In a first set of experiments we analysed the correlation of individual features with the average human response, obtaining correlations up to rs = .771. In a second set of experiments we employed Machine Learning techniques to predict the average visual complexity score attributed by humans to each stimuli. The best configurations obtained a correlation of rs = .832. The average prediction error of the Machine Learning system over the set of all stimuli was .096 in a normalized 0 to 1 interval, showing that it is possible to predict, with high accuracy human responses. Overall, edge density and compression error were the strongest predictors of human complexity ratings.

== Extended Abstract ==

== Bibtex ==
@article{Machado201543,
title = "Computerized measures of visual complexity ",
journal = "Acta Psychologica ",
volume = "160",
number = "",
pages = "43 - 57",
year = "2015",
note = "",
issn = "0001-6918",
doi = "http://dx.doi.org/10.1016/j.actpsy.2015.06.005",
url = "http://www.sciencedirect.com/science/article/pii/S0001691815300160 http://de.evo-art.org/index.php?title=Computerized_measures_of_visual_complexity ",
author = "Penousal Machado and Juan Romero and Marcos Nadal and Antonino Santos and João Correia and Adrián Carballal",
keywords = "Visual complexity",
keywords = "Psychological aesthetics",
keywords = "Vision",
keywords = "Machine learning "
}

== Used References ==
Alario, F. -X., & Ferrand, L. (1999). A set of 400 pictures standardized from French: Norms
for name agreement, image agreement, familiarity, visual complexity, image variability,
and age of acquisition. Behavior Research Methods, Instruments, & Computers, 31,
531–552.
https://www.researchgate.net/publication/12797319_A_set_of_400_pictures_standardized_for_French_Norms_for_name_agreement_image_agreement_familiarity_visual_complexity_image_variability_and_age_of_acquisition_Behavior_Research_Methods_Instruments_Comp

Arnheim, R. (1966). Towards a psychology of art/entropy and art—An essay on disorder and
order. The Regents of the University of California.

Attneave, F. (1957). Physical determinants of the judged complexity of shapes. Journal of
Experimental Psychology, 53, 221–227.

Bauerly, M., & Liu, Y. (2008). Effects of symmetry and number of compositional elements
on interface and design aesthetics. International Journal of Human Computer
Interaction, 24, 275–287.
https://www.researchgate.net/publication/288495065_Effects_of_Symmetry_and_Number_of_Compositional_Elements_on_Interface_and_Design_Aesthetics?el=1_x_8&enrichId=rgreq-ab579933c2c63fab322680cabd311339-XXX&enrichSource=Y292ZXJQYWdlOzI3OTk1NDY0MztBUzozMzIwMTE5MTE4Mjc0NjBAMTQ1NjE2OTIxNjc4OA==

Berlyne, D.E. (1963). Complexity and incongruity variables as determinants of exploratory
choice and evaluative ratings. Canadian Journal of Psychology, 17, 274–290.
https://www.researchgate.net/publication/9518071_Complexity_and_incongruity_variables_as_determinants_of_exploratory_choice_and_evaluative_ratings

Berlyne, D.E. (1970). Novelty, complexity, and hedonic value. Perception & Psychophysics,
8, 279–286.
https://www.researchgate.net/publication/225680366_Novelty_Complexity_and_Hedonic_Value?el=1_x_8&enrichId=rgreq-ab579933c2c63fab322680cabd311339-XXX&enrichSource=Y292ZXJQYWdlOzI3OTk1NDY0MztBUzozMzIwMTE5MTE4Mjc0NjBAMTQ1NjE2OTIxNjc4OA==

Berlyne, D.E. (1971). Aesthetics and psychobiology. New York: Appleton-Century-Crofts.
https://www.researchgate.net/publication/269451060_Aesthetics_and_Psychobiology?el=1_x_8&enrichId=rgreq-ab579933c2c63fab322680cabd311339-XXX&enrichSource=Y292ZXJQYWdlOzI3OTk1NDY0MztBUzozMzIwMTE5MTE4Mjc0NjBAMTQ1NjE2OTIxNjc4OA==

Berlyne, D.E. (1974). Novelty, complexity, and interestingness. In D.E. Berlyne (Ed.), Studies
in the new experimental aesthetics: Steps toward an objective psychology of aesthetic
appreciation (pp. 175–180). Washington, D. C.: Hemisphere Publishing Corporation.

Berlyne, D.E., Ogilvie, J.C., & Parham, L.C.C. (1968). The dimensionality of visual complexity,
interestingness, and pleasingness. Canadian Journal of Psychology, 22, 376–387.
https://www.researchgate.net/publication/17460547_The_dimensionality_of_visual_complexity_interestingness_and_pleasingness?el=1_x_8&enrichId=rgreq-ab579933c2c63fab322680cabd311339-XXX&enrichSource=Y292ZXJQYWdlOzI3OTk1NDY0MztBUzozMzIwMTE5MTE4Mjc0NjBAMTQ1NjE2OTIxNjc4OA==

Bertamini, M., Palumbo, L., Gheorghes, T.N., & Galatsidas, M. (2015). Do observers like
curvature or do they dislike angularity? British Journal of Psychology. http://dx.doi.org/10.1111/bjop.12132 (in press).
https://www.researchgate.net/publication/274903267_Do_observers_like_curvature_or_do_they_dislike_angularity?el=1_x_8&enrichId=rgreq-ab579933c2c63fab322680cabd311339-XXX&enrichSource=Y292ZXJQYWdlOzI3OTk1NDY0MztBUzozMzIwMTE5MTE4Mjc0NjBAMTQ1NjE2OTIxNjc4OA==

Birkhoff, G.D. (1932). Aesthetic measure. Cambridge, Mass.: Harvard University Press.

Bonin, P., Peereman, R., Malardier, N., Méot, A., & Chalard, M. (2003). A newset of 299 pictures
for psycholinguistic studies: French norms for name agreement, image agreement,
conceptual familiarity, visual complexity, image variability, age of acquisition,
and naming latencies. Behavior Research Methods, Instruments, & Computers, 35,
158–167.
https://www.researchgate.net/publication/10779789_A_new_set_of_299_pictures_for_psycholinguistic_studies_French_norms_for_name_agreement_image_agreement_conceptual_familiarity_visual_complexity_image_variability_age_of_acquisition_and_naming_latencie?el=1_x_8&enrichId=rgreq-ab579933c2c63fab322680cabd311339-XXX&enrichSource=Y292ZXJQYWdlOzI3OTk1NDY0MztBUzozMzIwMTE5MTE4Mjc0NjBAMTQ1NjE2OTIxNjc4OA==

Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern
Analysis and Machine Intelligence, PAMI-8(6).
Cela-Conde, C.J., Ayala, F.J., Munar, E., Maestú, F., Nadal, M., & Capó, M.A. (2009). Sexrelated
similarities and differences in the neural correlates of beauty. Proceedings of
the National Academy of Sciences of the United States of America, 106, 3847–3852.
Cela-Conde, C.J., Marty, G., Maestú, F., Ortiz, T., Munar, E., & Fernández, A. (2004). Activation
of the prefrontal cortex in the human visual aesthetic perception. Proceedings of
the National Academy of Sciences of the United States of America, 101, 6321–6325.
Chatterjee, A. (2004). Prospects for a cognitive neuroscience of visual aesthetics. Bulletin
of psychology and the arts, 4, 55–60.
Cottington, D. (1998). Cubism. London: Tate Gallery Publishing.
Cupchik, G.C. (1986). A decade after Berlyne. New directions in experimental aesthetics.
Poetics, 15, 345–369.
Donderi, D.C. (2003). A complexity measure for electronic displays: Final report on the experiments.
Toronto: Department of National Defence, Defence Research and Development
Canada.
Donderi, D.C. (2006). Visual complexity: A review. Psychological Bulletin, 132, 73–97.
Donderi, D.C., & McFadden, S. (2005). Compressed file length predicts search time and errors
on visual displays. Displays, 26, 71–78.
Eysenck, H.J. (1941). The empirical determination of an aesthetic formula. Psychological
Review, 48, 83–92.
Eysenck, H.J. (1942). The experimental study of the ‘Good Gestalt’ — A new approach.
Psychological Review, 49, 344–363.
Eysenck, H.J., & Castle, M. (Aug 1971). Comparative study of artists and nonartists on the
Maitland Graves design judgment test. Journal of Applied Psychology, 55(4), 389–392.
http://dx.doi.org/10.1037/h0031469.
Fechner, G.T. (1876). Vorschule der Ästhetik. Leipzig: Breitkopf und Härtel.
Fisher, Y. (Ed.). (1995). Fractal image compression: Theory and application. London:
Springer Verlag.
Forsythe, A., Mulhern, G., & Sawey, M. (2008). Confounds in pictorial sets: The role of
complexity and familiarity in basic-level picture processing. Behavior Research
Methods, 40, 116–129.
Forsythe, A., Nadal, M., Sheehy, N., Cela-Conde, C.J., & Sawey, M. (2011). Predicting beauty:
Fractal dimension and visual complexity in art. British Journal of Psychology, 102,
49–70.
Forsythe, A., Sheehy, N., & Sawey, M. (2003). Measuring icon complexity: An automated
analysis. Behavior Research Methods, Instruments, & Computers, 35, 334–342.
García, M., Badre, A.N., & Stasko, J.T. (1994). Development and validation of icons varying
in their abstractness. Interacting with computers, 6, 191–211.
Gooding, M. (2001). Abstract art. London: Tate Gallery Publishing.
Graves, M. (1948). Design judgement test. New York: The Psychological Corporation.
Haykin, S. (1994). Neural networks: A comprehensive foundation. Prentice Hall PTR.
Heath, T., Smith, S.G., & Lim, B. (2000). Tall buildings and the urban skyline. The effect of
visual complexity on preferences. Environment and Behavior, 32, 541–556.
Imamoglu, C. (2000). Complexity, liking and familiarity: Architecture and nonarchitecture
Turkish students' assessments of traditional and modern house facades.
Journal of Environmental Psychology, 20, 5–16.
Jones-Smith, K., & Mathur, H. (2006). Fractal analysis: Revisiting Pollock's drip paintings.
Nature, 444, E9–E10.
Krishen, A., Kamra, K., & Mac, F. (2008). Perceived versus actual complexity for websites:
Their relationship to consumer satisfaction. Journal of Consumer Satisfaction,
Dissatisfaction and Complaining Behavior, 21, 104–123.
Lang, P.J., Bradley, M.M., & Cuthbert, B.N. (2005). International affective picture system
(IAPS): Affective ratings of pictures and instruction manual. Technical report (pp. A
−7). Gainesville, FL: University of Florida.
Lavie, T., Oron-Gilad, T., & Meyer, J. (2011). Aesthetics and usability of in-vehicle navigation
displays. International Journal of Human-Computer Studies, 69, 80–89.
Lavie, T., & Tractinsky, N. (2004). Assessing dimensions of perceived visual aesthetics of
web sites. International Journal of Human-Computer Studies, 60, 269–298.
Leder, H., Belke, B., Oeberst, A., & Augustin, D. (2004). A model of aesthetic appreciation
and aesthetic judgments. British Journal of Psychology, 95, 489–508.
Leeuwenberg, E.L.J. (1968). Structural information of visual patterns: An efficient coding system
in perception. The Hague: Mouton.
Leeuwenberg, E.L.J. (1969). Quantitative specification of information in sequential patterns.
Psychological Review, 76, 216–220.
Machado, P. (2007). Inteligncia Artificial e Arte. (Ph.D. thesis) Coimbra, Portugal: University
of Coimbra (in Portuguese).
Machado, P., & Cardoso, A. (1998). Computing aesthetics. In F. Oliveira (Ed.), XIVth
Brazilian Symposium on Artificial Intelligence SBIA'98. LNAI Series. (pp. 219–229).
Porto Alegre, Brazil: Springer.
Machado, P., & Cardoso, A. (2002). All the truth about NEvAr. Applied Intelligence, Special
Issue on Creative Systems, 16(2), 101–119.
Machado, P., Romero, J., Cardoso, A., & Santos, A. (2005). Partially interactive evolutionary
artists. New Generation Computing, 23(42), 143–155.
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.), Springer Berlin Heidelberg.
Machado, P., Romero, J., Santos, A., Cardoso, A., & Pazos, A. (2007). On the development of
evolutionary artificial artists. Computers & Graphics, 31(6), 818–826.
Malpas, J. (1997). Realism. London: Tate Gallery Publishing.

Manaris, B., Purewal, T., & McCormick, C. (2002). Progress towards recognizing and classifying
beautiful music with computers—Midi-encoded music and the
zipfmandelbrot law. Proceedings of the IEEE southeastcon 2002 conference, Columbia.

Manaris, B., Vaughan, D., Wagner, C., Romero, J., & Davis, R.B. (2003). Evolutionary music
and the Zipf–Mandelbrot Law: Progress towards developing fitness functions for
pleasant music. EvoMUSART2003—1st European Workshop on Evolutionary Music and
Art, Essex, UK. Lecture Notes in Computer Science, Applications of Evolutionary
Computing, LNCS, 2611. (pp. 522–534). Springer.

Marin, M., & Leder, H. (2013). Examining complexity across domains: relating subjective
and objective measures of affective environmental scenes, paintings and music. PLoS
One, 8(8), e72412. http://dx.doi.org/10.1371/journal.pone.0072412.

McDougall, S.J.P., Curry, M.B., & de Bruijn, O. (1999). Measuring symbol and icon characteristics:
Norms for concreteness, complexity, meaningfulness, familiarity, and semantic
distance for 239 symbols. Behavior Research Methods, Instruments, &
Computers, 31, 487–519.

McDougall, S.J.P., de Bruijn, O., & Curry, M.B. (2000). Exploring the effects of icon characteristics
on user performance: The role of icon concreteness, complexity, and distinctiveness.
Journal of Experimental Psychology: Applied, 6, 291–306.

Moshagen, M., & Thielsch, M.T. (2010). Facets of visual aesthetics. International Journal of
Human-Computer Studies, 68, 689–709.

Nadal, M., Munar, E., Marty, G., & Cela-Conde, C.J. (2010). Visual complexity and beauty
appreciation: Explaining the divergence of results. Empirical Studies of the Arts, 28,
173–191.

Palmer, S.E. (1999). Vision Science: Photons to Phenomenology. MIT Press. 978-0-262-
16183-1.

Palmer, S.E., Schloss, K.B., & Sammartino, J. (2013). Visual aesthetics and human preference.
Annual Review of Psychology, 64, 77–107.

Palumbo, L., Ogden, R., Makin, A.D.J., & Bertamini,M. (2014). Examining visual complexity
and its influence on perceived duration. Journal of Vision, 14, 1–18. http://dx.doi.org/
10.1167/14.14.3.

Parr, M. (1999). Boring postcards. London: Phaidon Press.

Parr, M. (2000). Boring postcards USA. London: Phaidon Press.

Pecchinenda, A., Bertamini, M., Makin, A.D.J., & Ruta, N. (2014). The pleasantness of visual
symmetry: Always, never or sometimes. PLoS One, 9, e92685. http://dx.doi.org/10.1371/journal.pone.0092685.

Pieters, R., Wedel, M., & Batra, R. (2010). The stopping power of advertising: Measures
and effects of visual complexity. Journal of Marketing, 74, 48–60.

Powers, D. (1998). Applications and explanations of Zipf's law. NeMLaP3/CoNLL '98: Proceedings
of the joint conferences on new methods in language processing and computational
natural language learning (pp. 151–160). Morristown, NJ, USA: Association for
Computational Linguistics.

Reimann, M., Zaichkowsky, J., Neuhaus, C., Bender, T., &Weber, B. (2010). Aesthetic package
design: A behavioral, neural, and psychological investigation. Journal of Consumer
Psychology, 20, 431–441.

Rosenblatt, F. (Nov 1958). The perceptron: A probabilistic model for information storage
and organization in the brain. Psychological Review, 65(6), 386–408.

Rumelhart, D.E., Hinton, G.E., &Williams, R.J. (1986). Learning internal representations by
error propagation. In D.E. Rumelhart, J.L. McClelland, & PDP Research Group (Eds.),
Paralled Distributed Processing. Explorations in the Microstructure of Cognition.
Foundations, 1. (pp. 318–362). Cambridge, MA: The MIT Press.

Salomon, D. (1997). Data Compression: The Complete Reference. New York, NY, USA:
Springer-Verlag New York, Inc.

Simon, H.A. (1972). Complexity and the representation of patterned sequences of symbols.
Psychological Review, 79, 369–382.

Snodgrass, J.G. (1997). Picture naming by young children: Norms for name agreement, familiarity,
and visual complexity. Journal of Experimental Child Psychology, 65,
171–237.

Sobel, I. (1990). An isotropic 3 × 3 image gradient operator. Machine vision for threedimensional
scenes, 376–379.

Spehar, B., Clifford, C.W.G., Newell, B.R., & Taylor, R.P. (2003). Universal aesthetic of fractals.
Computers & Graphics, 27, 813–820.

Strother, L., & Kubovy, M. (2003). Perceived complexity and the grouping effect in band
patterns. Acta Psychologica, 114, 229–244.

Tatarkiewicz, W. (1972). The great theory of beauty and its decline. The Journal of
Aesthetics and Art Criticism, 31, 165–180.

Taylor, R.P., Micholich, A.P., & Jonas, D. (1999). Fractal analysis of Pollock's drip paintings.
Nature, 399, 422.

Taylor, R.P., Micholich, A.P., & Jonas, D. (2002). The construction of Jackson Pollock's fractal
drip paintings. Leonardo, 35, 203–207.

Thomson, B. (1998). Post-impressionism. London: Tate Gallery Publishing.

Tinio, P.P.L., & Leder, H. (2009). Just how stable are stable aesthetic features? Symmetry,
complexity, and the jaws of massive familiarization. Acta Psychologica, 130, 241–250.

van der Helm, P.A. (2004). Transparallel processing by hyperstrings. Proceedings of the
National Academy of Sciences of the United States of America, 101(30), 10862–10867.

van der Helm (2014). Simplicity in vision: A multidisciplinary account of perceptual organization.
Cambridge University Press.

Winston, A.S., & Cupchik, G.C. (1992). The evaluation of high art and popular art by naive
and experienced viewers. Visual Arts Research, 18, 1–14.

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

== Links ==

=== Full Text ===
https://www.researchgate.net/publication/279954643_Computerized_measures_of_visual_complexity

[[internal file]]

=== Sonstige Links ===

@@ Zeile 81: / Zeile 81: @@
 Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern
 Analysis and Machine Intelligence, PAMI-8(6).
 Cela-Conde, C.J., Ayala, F.J., Munar, E., Maestú, F., Nadal, M., & Capó, M.A. (2009). Sexrelated
 similarities and differences in the neural correlates of beauty. Proceedings of
 the National Academy of Sciences of the United States of America, 106, 3847–3852.
 Cela-Conde, C.J., Marty, G., Maestú, F., Ortiz, T., Munar, E., & Fernández, A. (2004). Activation
 of the prefrontal cortex in the human visual aesthetic perception. Proceedings of
 the National Academy of Sciences of the United States of America, 101, 6321–6325.
 Chatterjee, A. (2004). Prospects for a cognitive neuroscience of visual aesthetics. Bulletin
 of psychology and the arts, 4, 55–60.
 Cottington, D. (1998). Cubism. London: Tate Gallery Publishing.
 Cupchik, G.C. (1986). A decade after Berlyne. New directions in experimental aesthetics.
 Poetics, 15, 345–369.
 Donderi, D.C. (2003). A complexity measure for electronic displays: Final report on the experiments.
 Toronto: Department of National Defence, Defence Research and Development
 Canada.
 Donderi, D.C. (2006). Visual complexity: A review. Psychological Bulletin, 132, 73–97.
 Donderi, D.C., & McFadden, S. (2005). Compressed file length predicts search time and errors
 on visual displays. Displays, 26, 71–78.
 Eysenck, H.J. (1941). The empirical determination of an aesthetic formula. Psychological
 Review, 48, 83–92.
 Eysenck, H.J. (1942). The experimental study of the ‘Good Gestalt’ — A new approach.
 Psychological Review, 49, 344–363.
 Eysenck, H.J., & Castle, M. (Aug 1971). Comparative study of artists and nonartists on the
 Maitland Graves design judgment test. Journal of Applied Psychology, 55(4), 389–392.
 http://dx.doi.org/10.1037/h0031469.
 Fechner, G.T. (1876). Vorschule der Ästhetik. Leipzig: Breitkopf und Härtel.
 Fisher, Y. (Ed.). (1995). Fractal image compression: Theory and application. London:
 Springer Verlag.
 Forsythe, A., Mulhern, G., & Sawey, M. (2008). Confounds in pictorial sets: The role of
 complexity and familiarity in basic-level picture processing. Behavior Research
 Methods, 40, 116–129.
 Forsythe, A., Nadal, M., Sheehy, N., Cela-Conde, C.J., & Sawey, M. (2011). Predicting beauty:
 Fractal dimension and visual complexity in art. British Journal of Psychology, 102,
 –70.
 Forsythe, A., Sheehy, N., & Sawey, M. (2003). Measuring icon complexity: An automated
 analysis. Behavior Research Methods, Instruments, & Computers, 35, 334–342.
 García, M., Badre, A.N., & Stasko, J.T. (1994). Development and validation of icons varying
 in their abstractness. Interacting with computers, 6, 191–211.
 Gooding, M. (2001). Abstract art. London: Tate Gallery Publishing.
 Graves, M. (1948). Design judgement test. New York: The Psychological Corporation.
 Haykin, S. (1994). Neural networks: A comprehensive foundation. Prentice Hall PTR.
 Heath, T., Smith, S.G., & Lim, B. (2000). Tall buildings and the urban skyline. The effect of
 visual complexity on preferences. Environment and Behavior, 32, 541–556.
 Imamoglu, C. (2000). Complexity, liking and familiarity: Architecture and nonarchitecture
 Turkish students' assessments of traditional and modern house facades.
 Journal of Environmental Psychology, 20, 5–16.
 Jones-Smith, K., & Mathur, H. (2006). Fractal analysis: Revisiting Pollock's drip paintings.
 Nature, 444, E9–E10.
 Krishen, A., Kamra, K., & Mac, F. (2008). Perceived versus actual complexity for websites:
 Their relationship to consumer satisfaction. Journal of Consumer Satisfaction,
 Dissatisfaction and Complaining Behavior, 21, 104–123.
 Lang, P.J., Bradley, M.M., & Cuthbert, B.N. (2005). International affective picture system
 (IAPS): Affective ratings of pictures and instruction manual. Technical report (pp. A
 −7). Gainesville, FL: University of Florida.
 Lavie, T., Oron-Gilad, T., & Meyer, J. (2011). Aesthetics and usability of in-vehicle navigation
 displays. International Journal of Human-Computer Studies, 69, 80–89.
 Lavie, T., & Tractinsky, N. (2004). Assessing dimensions of perceived visual aesthetics of
 web sites. International Journal of Human-Computer Studies, 60, 269–298.
 Leder, H., Belke, B., Oeberst, A., & Augustin, D. (2004). A model of aesthetic appreciation
 and aesthetic judgments. British Journal of Psychology, 95, 489–508.
 Leeuwenberg, E.L.J. (1968). Structural information of visual patterns: An efficient coding system
 in perception. The Hague: Mouton.
 Leeuwenberg, E.L.J. (1969). Quantitative specification of information in sequential patterns.
 Psychological Review, 76, 216–220.
 Machado, P. (2007). Inteligncia Artificial e Arte. (Ph.D. thesis) Coimbra, Portugal: University
 of Coimbra (in Portuguese).
 Machado, P., & Cardoso, A. (1998). Computing aesthetics. In F. Oliveira (Ed.), XIVth
 Brazilian Symposium on Artificial Intelligence SBIA'98. LNAI Series. (pp. 219–229).
 Porto Alegre, Brazil: Springer.
 Machado, P., & Cardoso, A. (2002). All the truth about NEvAr. Applied Intelligence, Special
 Issue on Creative Systems, 16(2), 101–119.
 Machado, P., Romero, J., Cardoso, A., & Santos, A. (2005). Partially interactive evolutionary
 artists. New Generation Computing, 23(42), 143–155.
 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.), Springer Berlin Heidelberg.
 Machado, P., Romero, J., Santos, A., Cardoso, A., & Pazos, A. (2007). On the development of
 evolutionary artificial artists. Computers & Graphics, 31(6), 818–826.