Statistics, vision, and the analysis of artistic style

Wiley Interdisciplinary Reviews: Computational Statistics

Volumn 4, Issue 2, 2012, Pages 115-123

  Graham, Daniel J ^a   Hughes, James M ^b   Leder, Helmut ^a   Rockmore, Daniel N ^b,c  

^a UNIVERSITY OF VIENNA   (Austria)

^b Dartmouth College   (United States)

^c Lamont Doherty Earth Observatory   (United States)

Author keywords

Classification; Image processing; Logistic regression; Sparse coding; Stylometry

Indexed keywords

ART IMAGE; LOGISTIC REGRESSION; NATURAL SCENES; SPARSE CODING; STATISTICAL TECHNIQUES; STYLOMETRY; VISUAL-PROCESSING;

CLASSIFICATION (OF INFORMATION); IMAGE PROCESSING;

LOGISTICS;

EID: 84856712410     PISSN: 19395108     EISSN: 19390068     Source Type: Journal    
DOI: 10.1002/wics.197     Document Type: Article

References (47)

1. Spencer RD. The Expert versus the Object: Judging Fakes and False Attributions in the Visual Arts. Oxford: Oxford University Press; 2004.
2. Morelli G. Italian Painters: Critical Studies of their Works. London: J. Murray; 1892.
3. Wollheim R. Giovanni Morelli and the Origins of Scientific Connoisseurship. London: Allen Lane; 1973.
4. Ginzburg C. Morelli, Freud and Sherlock Holmes: clues and scientific method. Hist Workshop J 1980, 9:5-36.
5. Hockey S. The history of humanities computing. A Companion to Digital Humanities. Blackwell Publishing; 2004.
6. Lowe D. Object recognition from local scale-invariant features. Proc Seventh IEEE Conf Comput Vis 1999, 2, 1150-1157.
7. Rao A, Srihari R, Zhang Z. Spatial color histograms for content-based image retrieval. Proceedings of 11th IEEE International Conference on Tools with Artificial Intelligence, 1999, 183-186.
8. Gunsel B, Sariel S, Icoglu O. Content-based access to art paintings. IEEE International Conference on Image Processing, 2005. ICIP 2005, 2005, 2, 558-561.
9. Hughes JM, Graham DJ, Rockmore DN. Stylometrics of artwork: uses and limitations. Proceedings of SPIE: Computer Vision and Image Analysis of Art, 2010, 75310C1.
10. Li J, Yao L, Hendriks E, Wang JZ. Rhythmic brushstrokes distinguish Van Gogh from his contemporaries: findings via automated brushstroke extraction, 2011, submitted for publication.
11. Yao L, Li J, Wang J. Characterizing elegance of curves computationally for distinguishing Morrisseau paintings and the imitations. 16th IEEE International Conference on Image Processing (ICIP), 2009, 73-76.
12. Sablatnig R, Kammerer P, Zolda E. Hierarchical classification of paintings using face- and brush stroke models. Proceedings of Fourteenth International Conference on Pattern Recognition 1998, 1, 172-174.
13. Johnson JCR, Hendriks E, Berezhnoy I, Brevdo E, Hughes S, Daubechies I, Li J, Postma E, Wang J. Image processing for artist identification. IEEE Signal Process Mag 2008, 37:37-48.
14. van der Maaten L, Postma E. Identifying the real van Gogh with brushstroke textons, White paper, Tilburg University, February 2009.
15. Berezhnoy I, Postma E, van den Herik H. Authentic: computerized brushstroke analysis, 2005, 1586-1588.
16. Taylor RP, Micolich AP, Jonas D. Fractal analysis of Pollock's drip paintings. Nature 1999, 399:422.
17. Taylor R, Guzman R, Martin T, Hall G, Micolich A, Jonas D, Scannell B, Fairbanks M, Marlow C. Authenticating Pollock paintings using fractal geometry. Pattern Recognition Lett 2007, 28:695-702.
18. Jones-Smith K, Matthur H. Fractal analysis: revisiting Pollock's drip paintings. Nature 2006, 444:E9-E10.
19. Keren D. Painter identification using local features and naïve Bayes. Proceedings of the 16th International Conference on Pattern Recognition, 2002, 2, 474-477.
20. Lyu S, Rockmore D, Farid H. A digital technique for art authentication. Proc Natl Acad Sci 2004, 101:17006-17010.
21. Hughes JM, Mao D, Rockmore DN, Wang Y, Wu Q. Empirical mode decomposition analysis for visual stylometry, 2011, submitted for publication.
22. Lin L, Wang Y, Zhou H. Iterative filtering as an alternative algorithm for empirical mode decomposition. Adv Adapt Data Anal 2009, 1:543-560.
23. Temel B, Kilic N, Ozgultekin B, Ucan ON. Separation of original paintings of Matisse and his fakes using wavelet and artificial neural networks. Istanbul Univ J Elect Electron Eng 2009, 9:791-796.
24. Berezhnoy I, Postma E, van den Herik J. Computer analysis of van Gogh's complementary colours. Pattern Recognition Lett 2007, 28:703-709 Pattern Recognition in Cultural Heritage and Medical Applications.
25. Zujovic J, Gandy L, Friedman S, Pardo B, Pappas T. Classifying paintings by artistic genre: an analysis of features & classifiers. IEEE International Workshop on Multimedia Signal Processing, 2009. MMSP'09, 2009, 1-5.
26. Jones JP, Palmer LA. The two-dimensional spatial structure of simple receptive fields in cat striate cortex. J Neurophysiol 1987, 58:1187-1211.
27. Hubel DH, Wiesel TN. Receptive fields and functional architecture of monkey striate cortex. J Physiol 1968, 195:215-243.
28. Daugman JG. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am A 1985, 2:1160-1169.
29. Olshausen B, Field D. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 1996, 381:607-609.
30. Olshausen B, Field D. Sparse coding with an overcomplete basis set: a strategy employed by V1?. Vision Res 1997, 37:3311-3325.
31. Hughes JM, Graham DJ, Rockmore DN. Quantification of artistic style through sparse coding analysis in the drawings of Pieter Bruegel the Elder. Proc Natl Acad Sci 2010, 107:1279-1283.
32. Hughes JM, Graham DJ, Jacobsen CR, Rockmore DN. Comparing higher-order spatial statistics and perceptual judgements in the stylometric analysis of art. Proceedings of the 2011 European Signal Processing Conference, 2011.
33. Foti NJ, Hughes JM, Rockmore DN. Nonparametric sparsification of complex multiscale networks. PLoS One 2011, 6.
34. Graham DJ, Field DJ. Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities. Spatial Vision 2007, 21:149-164.
35. Redies C. Fractal-like image statistics in visual art: similarity to natural scenes. Spatial Vision 2007, 21: 137-148.
36. Barla A, Odone F, Verri A. Histogram intersection kernel for image classification. Proceedings of 2003 International Conference on Image Processing 2003, 3, 513-16.
37. Wölfflin H. Principles of Art History: The Problem and Development of Style in Later Art. New York: Dover; 1950.
38. Gombrich E. Art and Illusion: A Study in the Psychology of Pictorial Representation. London: Phaidon Press; 2001.
39. Arnheim R. Art and Visual Perception: A Psychology of the Creative Eye. Berkeley: University of California Press; 1954.
40. Gibson JJ. The information available in pictures. Leonardo 1971, 4:27-35.
41. Goodman N. Language of Art. Oxford: Oxford University Press; 1968.
42. Leder H, Belke B, Oeberst A, Augustin D. A model of aesthetic appreciation and aesthetic judgments. Brit J Psychol 2004, 95:489-508.
43. Bishop CM. Pattern Recognition and Machine Learning. 1st ed. New York: Springer; 2007.
44. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learn 2002, 43:389-422.
45. Graham D, Friedenberg J, Rockmore D, Field D. Mapping the similarity space of paintings: image statistics and visual perception. Visual Cogn 2009, 18:559-573.
46. Atick JJ, Redlich AN. What does the retina know about natural scenes? Neural Comput 1992, 4:196-210.
47. Graham DJ, Chandler DM, Field DJ. Can the theory of "whitening" explain the center-surround properties of retinal ganglion cell receptive fields? Vision Res 2006, 46:2901-2913.