How Self-Similar are Artworks at Different Levels of Spatial Resolution?
Seyed Ali Amirshahi, Christoph Redies, Joachim Denzler: How Self-Similar are Artworks at Different Levels of Spatial Resolution?. In: Donald H. House, Cindy Grimm (Eds.): Workshop on Computational Aesthetics, 2013. 93-100
Recent research has shown that a large variety of aesthetic paintings are highly self-similar. The degree of self-similarity seen in artworks is close to that observed for complex natural scenes, to which low-level visual coding in the human visual system is adapted. In this paper, we introduce a new measure of self-similarity, which we will refer to as the Weighted Self-Similarity (WSS). Using PHOG, which is a state-of-the-art technique from computer vision, WSS is derived from a measure that has been previously linked to aesthetic paintings and represents self-similarity on a single level of spatial resolution. In contrast, WSS takes into account the similarity values at multiple levels of spatial resolution. The values are linked to each other by using a weighting factor so that the overall self-similarity of an image reflects how self-similarity changes at different spatial levels. Compared to the previously proposed metric, WSS has the advantage that it also takes into account differences between self-similarity at different levels of spatial resolution with respect to one another.
An analysis of a large image dataset of aesthetic artworks (the JenAesthetics dataset) and other categories of images reveals that artworks, on average, show a relatively high WSS. Similarly, high values for WSS were obtained for images of natural patterns that can be described as being fractal (for example, images of clouds, branches or lichen growth patterns). The analysis of the JenAesthetics dataset, which consists of paintings of Western provenance, yielded similar values of WSS for different art styles. In conclusion, self-similarity is uniformly high across different levels of spatial resolution in the artworks analyzed in the present study.
Amirshahi, S. A., Koch, M., Denzler, J., and Redies, C. 2012. PHOG analysis of self-similarity in aesthetic images. In IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics, 82911J--82911J.
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.
Arnheim, R. 1954. Art and visual perception: A psychology of the creative eye. University of California Press.
Annalisa Barla , Emanuele Franceschi , Francesca Odone , Alessandro Verri, Image Kernels, Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, p.83-96, August 10, 2002 http://dl.acm.org/citation.cfm?id=719392&CFID=588525319&CFTOKEN=29804931
Berlyne, D. E. 1974. Studies in the new experimental aesthetics: Steps toward an objective psychology of aesthetic appreciation. Hemisphere Publishing Corporation.
Subhabrata Bhattacharya , Rahul Sukthankar , Mubarak Shah, A framework for photo-quality assessment and enhancement based on visual aesthetics, Proceedings of the international conference on Multimedia, October 25-29, 2010, Firenze, Italy http://doi.acm.org/10.1145/1873951.1873990
Birkhoff, G. D. 1933. Aesthetic measure. Cambridge, Mass.
Anna Bosch , Andrew Zisserman , Xavier Munoz, Representing shape with a spatial pyramid kernel, Proceedings of the 6th ACM international conference on Image and video retrieval, p.401-408, July 09-11, 2007, Amsterdam, The Netherlands http://doi.acm.org/10.1145/1282280.1282340
Burton, G., and Moorhead, I. R. 1987. Color and spatial structure in natural scenes. Applied Optics 26, 1, 157--170.
Navneet Dalal , Bill Triggs, Histograms of Oriented Gradients for Human Detection, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1, p.886-893, June 20-26, 2005 http://dx.doi.org/10.1109/CVPR.2005.177
Ritendra Datta , Dhiraj Joshi , Jia Li , James Z. Wang, Studying aesthetics in photographic images using a computational approach, Proceedings of the 9th European conference on Computer Vision, May 07-13, 2006, Graz, Austria http://dx.doi.org/10.1007/11744078_23
Field, D. J., et al. 1987. Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America 4, 12, 2379--2394.
Forsythe, A., Nadal, M., Sheehy, N., Cela-Conde, C. J., and Sawey, M. 2011. Predicting beauty: Fractal dimension and visual complexity in art. British Journal of Psychology 102, 1, 49--70.
Graham, D. J., and Field, D. J. 2007. Statistical regularities of art images and natural scenes: Spectra, sparseness and non-linearities. Spatial Vision 21, 1-2, 149--164.
Graham, D., and Redies, C. 2010. Statistical regularities in art: Relations with visual coding and perception. Vision Research 50, 16, 1503--1509.
Florian Hoenig, Defining computational aesthetics, Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging, May 18-20, 2005, Girona, Spain http://dx.doi.org/10.2312/COMPAESTH/COMPAESTH05/013-018
Jena. Jena computational aesthetics group. http://www.inf-cv.uni-jena.de/en/aesthetics.
JenAesthetics. JenAesthetics dataset. http://www.inf-cv.uni-jena.de/en/jenaesthetics.
Koch, M., Denzler, J., and Redies, C. 2010. 1/f2 characteristics and isotropy in the Fourier power spectra of visual art, cartoons, comics, mangas, and different categories of photographs. PloS One 5, 8, e12268.
Li, C., and Chen, T. 2009. Aesthetic visual quality assessment of paintings. Selected Topics in Signal Processing, IEEE Journal of 3, 2, 236--252.
Melmer, T., Amirshahi, S. A., Koch, M., Denzler, J., and Redies, C. 2013. From regular text to artistic writing and artworks: Fourier statistics of images with low and high aesthetic appeal. Frontiers in Human Neuroscience 7, 106.
J. R. Mureika , R. P. Taylor, The Abstract Expressionists and Les Automatistes: A shared multi-fractal depth?, Signal Processing, v.93 n.3, p.573-578, March, 2013 http://dx.doi.org/10.1016/j.sigpro.2012.05.002
Reber, R., Schwarz, N., and Winkielman, P. 2004. Processing fluency and aesthetic pleasure: is beauty in the perceiver's processing experienc? Personality and Social Psychology Review 8, 4, 364--382.
Redies, C., Hasenstein, J., and Denzler, J. 2007. Fractal-like image statistics in visual art: similarity to natural scenes. Spatial Vision 21, 1-2, 137--148.
Christoph Redies , Seyed Ali Amirshahi , Michael Koch , Joachim Denzler, PHOG-derived aesthetic measures applied to color photographs of artworks, natural scenes and objects, Proceedings of the 12th international conference on Computer Vision, October 07-13, 2012, Florence, Italy [doi>10.1007/978-3-642-33863-2_54]
Redies, C. 2007. A universal model of esthetic perception based on the sensory coding of natural stimuli. Spatial Vision 21, 1-2, 97--117.
Jaume Rigau , Miquel Feixas , Mateu Sbert, Informational Aesthetics Measures, IEEE Computer Graphics and Applications, v.28 n.2, p.24-34, March 2008 http://dx.doi.org/10.1109/MCG.2008.34
R. P. Taylor , R. Guzman , T. P. Martin , G. D. R. Hall , A. P. Micolich , D. Jonas , B. C. Scannell , M. S. Fairbanks , C. A. Marlow, Authenticating Pollock paintings using fractal geometry, Pattern Recognition Letters, v.28 n.6, p.695-702, April, 2007 http://dx.doi.org/10.1016/j.patrec.2006.08.012
Christian Wallraven , Roland Fleming , Douglas Cunningham , Jaume Rigau , Miquel Feixas , Mateu Sbert, Computational Aesthetics 2008: Categorizing art: Comparing humans and computers, Computers and Graphics, v.33 n.4, p.484-495, August, 2009 http://dx.doi.org/10.1016/j.cag.2009.04.003
Xue, S., Lin, Q., Tretter, D., Lee, S., Pizlo, Z., and Allebach, J. 2012. Investigation of the role of aesthetics in differentiating between photographs taken by amateur and professional photographers. In Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 8302, 7.
Zeki, S. 1999. Art and the brain. Journal of Consciousness Studies 6, 6-7, 76--96.