@inproceedings{Koch09:PCA,
    type = {inproceedings},
    key = {Koch09:PCA},
    title = {Principal component analysis of fourier transforms discriminates visual art from other image categories},
    author = {Michael Koch and Joachim Denzler and Christoph Redies},
    booktitle = {ECVP Abstract Supplement},
    year = {2009},
    month = {08},
    number = {38},
    pages = {49},
    abstract = {On average, natural scenes show a unique property in the Fourier domain--a roughly 1/f2 power spectrum, to which processing in the human visual system is optimally adapted. Recent studies reported similar properties in visual art of different styles and epochs. Here, we compared various datasets of photographs (natural scenes, objects, faces) and man-made images (art, cartoons, comics, scientific illustrations). Each dataset contained at least 150 images (1800 images in total). Results showed that, like art and natural scenes, cartoons and comics possess roughly 1/f2 power spectra. Principal component analysis of the 2-D power spectra revealed statistical differences between the image categories that were verified pairwise by significance testing. The resulting frequency-domain eigenspace achieved a good separation of the diverse categories. Principal component analysis carried out separately for each category showed that the first components of the art categories (graphic art, portraits and paintings) were similar, despite large differences in subject matters and artistic techniques. The power spectra of art images can be fitted well to a model that assumes 1/f2 characteristics and isotropy. In conclusion, art images display properties in the Fourier domain that allow to distinguish them from other image categories.},
    groups = {aesthetics},
    keywords = {pca, image statistics, aesthetics, visual art},
    website = {www.perceptionweb.com/abstract.cgi?id=v090318},
}