Dr. Daniel J. Graham Profile

Dr. Daniel J. Graham

Post-Doctoral Researcher at Univ Wien

SPIE Involvement:

Author

Proceedings Article | 18 February 2010 Paper

Preference for art: similarity, statistics, and selling price

Daniel Graham, Jay Friedenberg, Cyrus McCandless, Daniel Rockmore

Proceedings Volume 7527, 75271A (2010) https://doi.org/10.1117/12.842398

KEYWORDS: Visualization, Image compression, Visual process modeling, Statistical modeling, Visual system, Spatial frequencies, Data modeling, Image processing, Systems modeling, Databases

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Factors governing human preference for artwork have long been studied but there remain many holes in our understanding. Bearing in mind contextual factors (both the conditions under which the art is viewed, and the state of knowledge viewers have regarding art) that play some role in preference, we assess in this paper three questions. First, what is the relationship between perceived similarity and preference for different types of art? Second, are we naturally drawn to certain qualities-and perhaps to certain image statistics-in art? And third, do social and economic forces tend to select preferred stimuli, or are these forces governed by non-aesthetic factors such as age, rarity, or artist notoriety? To address the first question, we tested the notion that perceived similarity predicts preference for three classes of paintings: landscape, portrait/still-life, and abstract works. We find that preference is significantly correlated with (a) the first principal component of similarity in abstract works; and (b) the second principal component for landscapes. However, portrait/still-life images did not show a significant correlation between similarity and preference, perhaps due to effects related to face perception. The preference data were then compared to a wide variety of image statistics relevant to early visual system coding. For landscapes and abstract works, nonlinear spatial and intensity statistics relevant to visual processing explained surprisingly large portions of the variance of preference. For abstract works, a quarter of the variance of preference rankings could be explained by a statistic gauging pixel sparseness. For landscape paintings, spatial frequency amplitude spectrum statistics explained one fifth of the variance of preference data. Consistent with results for similarity, image statistics for portrait/still-life works did not correlate significantly with preference. Finally, we addressed the role of value. If there are shared "rules" of preference, one might expect "free markets" to value art in proportion to its aesthetic appeal, at least to some extent. To assess the role of value, a further test of preference was performed on a separate set of paintings recently sold at auction. Results showed that the selling price of these works showed no correlation with preference, while basic statistics were significantly correlated with preference. We conclude that selling price, which could be seen as a proxy for a painting's "value," is not predictive of preference, while shared preferences may to some extent be predictable based on image statistics. We also suggest that contextual and semantic factors play an important role in preference given that image content appears to lead to greater divergence between similarity and preference ratings for representational works, and especially for artwork that prominently depicts faces. The present paper paves the way for a more complete understanding of the relationship between shared human preferences and image statistical regularities, and it outlines the basic geometry of perceptual spaces for artwork.

Proceedings Article | 16 February 2010 Paper

Stylometrics of artwork: uses and limitations

James Hughes, Daniel Graham, Daniel Rockmore

Proceedings Volume 7531, 75310C (2010) https://doi.org/10.1117/12.838849

KEYWORDS: Statistical analysis, Facial recognition systems, Image analysis, Detection and tracking algorithms, Analytical research, Image compression, RGB color model, Nondestructive evaluation, Statistical modeling, Image filtering

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Proceedings Article | 10 February 2009 Paper

Efficient visual system processing of spatial and luminance statistics in representational and non-representational art

Daniel Graham, Jay Friedenberg, Daniel Rockmore

Proceedings Volume 7240, 72401N (2009) https://doi.org/10.1117/12.817185

KEYWORDS: Visualization, Visual system, Transform theory, High dynamic range imaging, Image compression, Image filtering, Statistical analysis, Light sources and illumination, Spatial frequencies, Calibration

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An emerging body of research suggests that artists consistently seek modes of representation that are efficiently processed by the human visual system, and that these shared properties could leave statistical signatures. In earlier work, we showed evidence that perceived similarity of representational art could be predicted using intensity statistics to which the early visual system is attuned, though semantic content was also found to be an important factor. Here we report two studies that examine the visual perception of similarity. We test a collection of non-representational art, which we argue possesses useful statistical and semantic properties, in terms of the relationship between image statistics and basic perceptual responses. We find two simple statistics-both expressed as single values-that predict nearly a third of the overall variance in similarity judgments of abstract art. An efficient visual system could make a quick and reasonable guess as to the relationship of a given image to others (i.e., its context) by extracting these basic statistics early in the visual stream, and this may hold for natural scenes as well as art. But a major component of many types of art is representational content. In a second study, we present findings related to efficient representation of natural scene luminances in landscapes by a well-known painter. We show empirically that elements of contemporary approaches to high-dynamic range tone-mapping-which are themselves deeply rooted in an understanding of early visual system coding-are present in the way Vincent Van Gogh transforms scene luminances into painting luminances. We argue that global tone mapping functions are a useful descriptor of an artist's perceptual goals with respect to global illumination and we present evidence that mapping the scene to a painting with different implied lighting properties produces a less efficient mapping. Together, these studies suggest that statistical regularities in art can shed light on visual processing.

Proceedings Article | 3 March 2008 Paper

Global nonlinear compression of natural luminances in painted art

Daniel Graham, David Field

Proceedings Volume 6810, 68100K (2008) https://doi.org/10.1117/12.766636

KEYWORDS: Image compression, Calibration, Visual system, Photography, Cameras, High dynamic range imaging, Reflectivity, CCD cameras, Camera shutters, Projection systems

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Conference Committee Involvement (2)

Computer Vision and Image Analysis of Art II

26 January 2011 | San Francisco Airport, California, United States

Computer Vision and Image Analysis of Art

18 January 2010 | San Jose, California, United States

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