Skip to main content
SpringerLink
Log in

Distinguishing paintings from photographs by complexity estimates

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This study is aimed at exploring the ability of complexity-based metrics to distinguish between paintings and photographs. The proposed features resort to edge detection, compression and entropy estimate methods that are highly correlated with artwork complexity. Artificial neural networks based on these features were trained for this task. The relevance of various combinations of these complexity metrics is also analyzed. The results of the current study indicate that different estimates related to image complexity achieve better results than state-of-the-art feature sets based on color, texture and perceptual edges. The classification success rate achieved is 94.82% on a dataset of 5235 images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Aks DJ, Sprott JC (1996) Quantifying aesthetic preference for chaotic patterns. Empir Stud Arts 14(1):1–16

    Article  Google Scholar 

  2. Arnheim R (1954) Art and visual perception: a psychology of the creative eye. Univ of California Press, California

    Google Scholar 

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

    Google Scholar 

  4. Athitsos V, Swain MJ, Frankel C (1997) Distinguishing photographs and graphics on the world wide web. Content-Based Access of Image and Video Libraries, 1997. Proceedings. IEEE Workshop on, IEEE, pp 10–17

  5. Birkhoff GD (1933) Aesthetic measure. Mass, Cambridge

    Book  MATH  Google Scholar 

  6. Blum A (1992) Neural networks in C++: an object-oriented framework for building connectionist systems. Wiley, New York

    Google Scholar 

  7. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  Google Scholar 

  8. Cutzu F, Hammoud R, Leykin A (2003) Estimating the photorealism of images: distinguishing paintings from photographs. In: Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, IEEE, 2: II–305

  9. Cutzu F, Hammoud R, Leykin A (2005) Distinguishing paintings from photographs. Comput Vision Image Underst 100(3):249–273

    Article  Google Scholar 

  10. Datta R, Joshi D, Li J, Wang JZ (2006) Studying aesthetics in photographic images using a computational approach. In: Leonardis A, Bischof H, Pinz A (eds) Computer Vision – ECCV 2006. Lecture notes in computer science, vol 3953. Springer, Berlin, Heidelberg, pp 288–301

    Chapter  Google Scholar 

  11. Eysenck HJ (1941) The empirical determination of an aesthetic formula. Psychol Rev 48(1):83

    Article  Google Scholar 

  12. Eysenck HJ (1942) The experimental study of the’good gestalt’? A new approach. Psychol Rev 49(4):344

    Article  Google Scholar 

  13. Fisher Y (1994) Fractal image compression. Fractals 2(03):347–361

    Article  MATH  Google Scholar 

  14. Forsythe A, Nadal M, Sheehy N, Cela-Conde CJ, Sawey M (2011) Predicting beauty: fractal dimension and visual complexity in art. Br J Psychol 102(1):49–70

    Article  Google Scholar 

  15. Greenfield G, Machado P (2009) Simulating artist and critic dynamics—an agent-based application of an evolutionary art system. In: Dourado A, Rosa AC, Madani K (eds) IJCCI 2009—Proceedings of the international joint conference on computational intelligence. INSTICC Press, Funchal, Madeira, pp 190–197

    Google Scholar 

  16. Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863

    Article  Google Scholar 

  17. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI Morgan Kaufmann, Burlington, pp 1137–1145

    Google Scholar 

  18. Machado P, Cardoso A (1998) Computing aesthetics. In: de Oliveira FM (ed) Advances in artificial intelligence. SBIA 1998. Lecture notes in computer science, vol 1515. Springer, Berlin, Heidelberg, pp 219–228

    Google Scholar 

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

    Article  MATH  Google Scholar 

  20. Machado P, Romero J, Santos ML, Cardoso A, Manaris B (2004) Adaptive critics for evolutionary artists. In: Raidl GR et al (eds) Applications of evolutionary computing. EvoWorkshops 2004. Lecture notes in computer science, vol 3005. Springer, Berlin, Heidelberg, pp 437–446

    Google Scholar 

  21. Machado P, Romero J, Manaris B (2008) Experiments in computational aesthetics. In: The art of artificial evolution, Springer, pp 381–415

  22. Machado P, Romero J, Nadal M, Santos A, Correia J, Carballal A (2015) Computerized measures of visual complexity. Acta Psychol 160:43–57. doi:10.1016/j.actpsy.2015.06.005, http://www.sciencedirect.com/science/article/pii/S0001691815300160

  23. Manaris B, Purewal T, McCormick C (2002) Progress towards recognizing and classifying beautiful music with computers-midi-encoded music and the Zipf-Mandelbrot law. SoutheastCon, 2002. Proceedings IEEE, IEEE, pp 52–57

  24. Manaris B, Romero J, Machado P, Krehbiel D, Hirzel T, Pharr W, Davis RB (2005) Zipf’s law, music classification, and aesthetics. Comput Music J 29(1):55–69

    Article  Google Scholar 

  25. 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 National Conference on Artificial Intelligence, Vol 1, AAAI Press, AAAI’07, pp 839–845

  26. Meier NC (1942) Art in human affairs; an introduction to the psychology of art. McGraw-Hill, New York

    Google Scholar 

  27. Moles AA (1957) Théorie de l’information et perception esthétique. Rev Philos de la France et de l’Étranger 147:233–242

    Google Scholar 

  28. Nadal Roberts M (2007) Complexity and aesthetic preference for diverse visual stimuli. Ph.D. thesis, Universitat de les Illes Balears

  29. Powers DM (1998) Applications and explanations of Zipf’s law. In: Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning, Association for Computational Linguistics, pp 151–160

  30. Rigau J, Feixas M, Sbert M (2005) An information-theoretic framework for image complexity. In: Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics. Visualization and Imaging, Eurographics Association, pp 177–184

  31. Romero J, Machado P, Carballal A, Osorio O (2011) Aesthetic classification and sorting based on image compression. In: Di Chio C et al (eds) Applications of evolutionary computation. EvoApplications 2011. Lecture notes in computer science, vol 6625. Springer, Berlin, Heidelberg, pp 394–403

    Chapter  Google Scholar 

  32. Romero J, Machado P, Carballal A, Correia J (2012a) Computing aesthetics with image judgement systems, Springer, Berlin, pp 295–322. doi:10.1007/978-3-642-31727-9_11.

  33. Romero J, Machado P, Carballal A, Santos A (2012b) Using complexity estimates in aesthetic image classification. J Math Arts 6(2–3):125–136

    Article  MathSciNet  Google Scholar 

  34. Rumelhart DE, Hinton GE, Williams RJ (1988) Neurocomputing: foundations of research. MIT Press, Cambridge. chap Learning representations by back-propagating errors, pp 696–699

  35. Salingaros NA, West BJ (1999) A universal rule for the distribution of sizes. Environ Plan B 26:909–924

    Article  Google Scholar 

  36. Saunders R, Gero JS (2001) Artificial creativity: a synthetic approach to the study of creative behaviour. Computational and cognitive models of creative design V, Key Centre of Design Computing and Cognition. University of Sydney, Sydney, pp 113–139

    Google Scholar 

  37. Schiffmann W, Joost M, Werner R (1994) Optimization of the backpropagation algorithm for training multilayer perceptrons. Technical report, University of Koblenz, Institute of Physics, Rheinau

  38. Sobel I (1990) An isotropic 3 \(\times\) 3 image gradient operator. Mach Vision Three Dimens Sci 3:376–379

    Google Scholar 

  39. Spehar B, Clifford CW, Newell BR, Taylor RP (2003) Universal aesthetic of fractals. Comput Gr 27(5):813–820

    Article  Google Scholar 

  40. 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: Raidl GR et al (eds) Applications of evolutionary computing. Lecture notes in computer science, vol 3005. Springer, Berlin, Heidelberg, pp 447–456

    Chapter  Google Scholar 

  41. Taylor RP, Micolich AP, Jonas D (1999) Fractal analysis of pollock’s drip paintings. Nature 399(6735):422–422

    Article  Google Scholar 

  42. Voss RF, Clarke J (1978) “1/f noise” in music: music from 1/f noise. J Acoust Soc Am 63:258

    Article  Google Scholar 

  43. Zipf GK (1949) Human behavior and the principle of least effort. Addison-Wesley press, Boston

    Google Scholar 

Download references

Acknowledgements

We are very grateful for the reviewers suggestions. This work was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. GRC2014/049) and the European Fund for Regional Development (FEDER) in the European Union, the Portuguese Foundation for Science and Technology in the scope of project SBIRC (Ref. PTDC/EIA EIA/115667/2009), Xunta de Galicia (Ref. XUGA - PGIDIT - 10TIC105008-PR) and the Spanish Ministry for Science and Technology (Ref. TIN2008-06562/TIN).

Author information

Authors and Affiliations

  1. Artificial Neural Networks and Adaptive Systems LAB, University of A Coruña, 15071, A Coruña, Spain

    Adrian Carballal, Antonino Santos, Juan Romero & Luz Castro

  2. CISUC, Department of Informatics Engineering, University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado & João Correia

Authors
  1. Adrian Carballal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonino Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Penousal Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. João Correia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Luz Castro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonino Santos.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carballal, A., Santos, A., Romero, J. et al. Distinguishing paintings from photographs by complexity estimates. Neural Comput & Applic 30, 1957–1969 (2018). https://doi.org/10.1007/s00521-016-2787-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2787-5

Keywords

Search

Navigation