The statistics of style

paintings or drawings, what comes to mind are specific brush or pen strokes, the manner in which objects are shaded, or how characters or landscapes are portrayed. Art historians are skilled at identifying such details through visual inspection, and art collectors and museums currently rely on this type of expert analysis to authenticate works of art. Might it be possible to automate this process to provide a more objective assessment? Is it possible to teach a computer to analyse art? In an article in Proceedings of the National Academy of Sciences, Hughes et al. 1 demonstrate that subtle stylistic differences between the paintings of Pieter Bruegel the Elder and those of his imitators, which were at one time misattri b-uted by art historians, may be reliably detected by statistical methods. Hughes and colleagues' work is the latest in a stream of research findings that have emerged over the past few decades in the field of 'image statistics'. The players in this field are an unlikely cadre of engineers, statisticians and neuroscientists who are seeking to characterize what makes images of the natural environment different from unstructured or random images (such as the 'static' on a computer monitor or television). Answering this question is central to the problem of coding and transmitting images over the airwaves and the Internet, and, it turns out, it is just as important for understanding how neurons encode and represent images in the brain. The first image statisticians were television engineers, who, as early as the 1950s, were trying to exploit correlations in television signals to compress the signals into a more efficient format. Around the same time, pioneering psychologists and neuroscientists such as Fred Attneave and Horace Barlow were using ideas from information theory to work out how the particular structures contained in images shape the way that information is coded by neurons in the brain. Since then, others have succeeded in developing specific mathematical models of natural-image structure — showing, for example , that the two-dimensional power spectrum varies with spatial frequency, f, roughly as 1/f 2 (ref. 2), and that the distribution of contrast in local image regions is invariant across scale 3–5. Investigators also began applying these and related models to characterize the statistical structure of paintings by particular artists. It was shown, for example, that Jackson Pollock's drip paintings have fractal structure 6 , and that Bruegel's drawings could be distinguished …