Cookies Policy
X

This site uses cookies. By continuing to browse the site you are agreeing to our use of cookies.

I accept this policy

Find out more here

Visual Image Statistics in the History of Western Art

No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.

Brill’s MyBook program is exclusively available on BrillOnline Books and Journals. Students and scholars affiliated with an institution that has purchased a Brill E-Book on the BrillOnline platform automatically have access to the MyBook option for the title(s) acquired by the Library. Brill MyBook is a print-on-demand paperback copy which is sold at a favorably uniform low price.

Access this advance article

+ Tax (if applicable)
Add to Favorites
You must be logged in to use this functionality
image of Art & Perception

Affiliations: 1: School of Psychology, University of Lincoln, Brayford Wharf East, Lincoln LN5 7AY, UK

10.1163/22134913-20181092
/content/journals/10.1163/22134913-20181092
dcterms_title,pub_keyword,dcterms_description,pub_author
10
5
Loading
Loading

Full text loading...

/content/journals/10.1163/22134913-20181092
Loading

Data & Media loading...

http://brill.metastore.ingenta.com/content/journals/10.1163/22134913-20181092
Loading

Article metrics loading...

/content/journals/10.1163/22134913-20181092
2018-06-28
2018-10-16
1. Aks D. J., and Sprott J. C. (1996). "Quantifying aesthetic preference for chaotic patterns", Empir. Stud. Arts Vol 14, 116.
2. Amirshahi S. A.,, Denzler J., and Redies C. (2013). JenAesthetics — A public dataset of paintings for aesthetic research. Tech. rep., Computer Vision Group, University of Jena, Germany, pp. 112.
3. Amirshahi S. A.,, Hayn-Leichsenring G. U.,, Denzler J., and Redies C., (2015). "JenAesthetics subjective dataset: Analyzing paintings by subjective scores", in: Agapito L.,, Bronstein M., and Rother C. (Eds), Computer Vision — ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science , Vol. 8925, pp. 319. Springer, Cham, Switzerland. http://doi.org/10.1007/978-3-319-16178-5_1.
4. Brettell R. R. (1999). Modern Art 1851–1929 . Oxford University Press, Oxford, UK.
5. Burton G. J., and Moorhead I. R. (1987). "Color and spatial structure in natural scenes", Appl. Opt. Vol 26, 157170.
6. Denis M. (1890, August). "Définition du néo-traditionnisme", Art Critique Vol 65, 540542.
7. Elger D. (2017). Abstract Art . Taschen, Koln, Germany.
8. Field D. J. (1987). "Relations between the statistics of natural images and the response properties of cortical cells", J. Opt. Soc. Am. A Vol 4, 23792394.
9. Gombrich E. (1972). The Story of Art , 13th ed. Phaedon, Oxford, UK.Graham, D. J. and Field, D. J. (2007). "Statistical regularities of art images and natural scenes: Spectra, sparseness and nonlinearities", Spat. Vis.Vol 21, 149164.
10. Graham D. J., and Field D. J. (2008). "Variations in intensity statistics for representational and abstract art, and for art from the Eastern and Western hemispheres", Perception Vol 37, 13411352.
11. Graham-Dixon A. (2008). Art: The Definitive Visual Guide . Dorling Kindersley, London, UK.
12. Güçlütürk Y.,, Jacobs R. H. A. H., and Lier R. van (2016). "Liking versus complexity: Decomposing the inverted U-curve", Front. Hum. Neurosci. Vol 10, 112. http://doi.org/10.3389/fnhum.2016.00112.
13. Hayn-Leichsenring G. U.,, Lehmann T., and Redies C. (2017). Subjective ratings of beauty and aesthetics: correlations with statistical image properties in Western oil paintings, iPerception 8, 2041669517715474. http://doi.org/10.1177/2041669517715474.
14. Jin X. C., and Ong S. H. (1995). "A practical method for estimating fractal dimension", Pattern Recognit. Lett. Vol 16, 457464.
15. Kersten D. (1987). "Predictability and redundancy of natural images", J. Opt. Soc. Am. A Vol 4, 23952400.
16. Knill D. C.,, Field D. J., and Kersten D. (1990). "Human discrimination of fractal images", J. Opt. Soc. Am. A Vol 7, 11131123.
17. Li J.,, Du Q., and Sun C. (2009). "An improved box-counting method for image fractal dimension estimation", Pattern Recognit. Vol 42, 24602469.
18. Little S. (2004). - isms: Understanding Art . Bloomsbury, London, UK.
19. Liu Y.,, Chen L.,, Wang H.,, Jiang L.,, Zhang Y.,, Zhao J.,, Wang D.,, Zhao Y., and Song Y. (2014). "An improved differential box-counting method to estimate fractal dimensions of gray-level images", J. Vis. Commun. Image Represent. Vol 25, 11021111.
20. Mather G. (2014). "Artistic adjustment of image spectral slope". Art Percept. Vol 2, 1122.
21. Olmos A., and Kingdom F. A. A. (2004). "A biologically inspired algorithm for the recovery of shading and reflectance images", Perception Vol 33, 14631473.
22. Peitgen D., and Saupe H. (1988). The Science of Fractal Images . Springer-Verlag, New York, NY, USA.
23. Phillips S. (2012). …isms. Understanding Modern Art . Bloomsbury, London, UK.
24. Redies C. (2007). "A universal model of esthetic perception based on the sensory coding of natural stimuli", Spat. Vis. Vol 21, 97117.
25. Redies C., and Brachmann A. (2017). "Statistical image properties in large subsets of traditional art, bad art, and abstract art", Front. Neurosci. Vol 11, 593. http://doi.org/10.3389/fnins.2017.00593.
26. Redies C.,, Hänisch J.,, Blickhan M., and Denzler J. (2007a). "Artists portray human faces with the Fourier statistics of complex natural scenes", Network Vol 18, 235248.
27. Redies C.,, Hasenstein J., and Denzler J. (2007b). "Fractal-like image statistics in visual art: similarity to natural scenes", Spat. Vis. Vol 21, 137148.
28. Ruderman D. L. (1994). "The statistics of natural images", Network Vol 5, 517548.
29. Ruderman D. L. (1997). "Origins of scaling in natural images", Vis. Res. Vol 37, 33853398.
30. Sartori A. (2014). "Affective analysis of abstract paintings using statistical analysis and art theory", in: Proceedings of the 16th International Conference on Multimodal Interaction - ICMI ’14, V(212), Istanbul, Turkey, pp. 384388. http://doi.org/10.1145/2663204.2666289.
31. Saupe D., (1988). "Algorithms for random fractals", in: The Science of Fractal Images , Peitgen D., and Saupe H. (Eds), pp. 71136. Springer-Verlag, New York, NY, USA.
32. Schweinhart A. M.,, and Essock E. A. (2013). "Structural content in paintings: Artists overregularize oriented content of paintings relative to the typical natural scene bias", Perception Vol 42, 13111332.
33. Shannon C. E. (1948). "A mathematical theory of communication", Bell Syst. Tech. J. Vol 27, 379–423, 623656.
34. Spehar B.,, Clifford C. W. G.,, Newell B. R., and Taylor R. P. (2003). "Universal aesthetic of fractals", Comput. Graph. Vol 27, 813820.
35. Spehar B.,, Walker N., and Taylor R. P. (2016). "Taxonomy of individual variations in aesthetic responses to fractal patterns", Front. Hum. Neurosci. Vol 10, 350. http://doi.org/10.3389/fnhum.2016.00350.
36. Yanulevskaya V.,, Uijlings J.,, Bruni E.,, Sartori A.,, Zamboni E.,, Bacci F.,, Melcher D., and Sebe N. (2012). "In the eye of the beholder: Employing statistical analysis and eye tracking for analyzing abstract paintings categories and subject descriptors", in: Proceedings of the 20th ACM International Conference on Multimedia , Nara, Japan, pp. 349358. http://doi.org/10.1145/2393347.2393399.
Submit comment
Close
Comment moderation successfully completed

Sign-in

Can't access your account?
  • Key

  • Full access
  • Open Access
  • Partial/No accessInformation