Statistical Analysis and Transfer of Pictorial Styles

Humans read content from images. However, images have not only content, but also a notion of style. Some pictorial styles differ noticeably and can be distinguished without careful inspection of the picture. This paper analyzes the style that can be noticeable at a pre-attentive level, using image statistics. Since the way images are decoded in the human visual system is related to the structure of visual data in the images, we examine the statistical properties of images. It has been found that visual data in natural images is highly structured and statistical tools have been frequently used to parameterize the structure. We extend the range of statistical analysis to parameterize and transfer simple notions of pictorial style and ambiance in images. Furthermore, it has been shown that images are processed in the human visual system using oriented linear filters. Therefore, this paper will exploit steerable pyramids, which have multiscale and oriented subbands resembling simple cell receptive fields. We show that the average of the absolute steerable coefficients, as a function of the scale, characterizes simple notions of composition and style.