A self organizing map based method for understanding features associated with high aesthetic value evolved abstract images

Abstract

We show a method that allows the pixel data of a set of images to be analyzed independently of any set of computed features. If the high and low aesthetic value images can be separated in the high dimensional space of pixel intensities then for any given set of features computed from the images, those features relevant to high aesthetic value can be determined and the range of feature values that correlate with high aesthetic appeal can be quantified. The method uses the Self Organizing Map to project raw pixel data of images onto a feature map. The aesthetic class of these images is overlayed on the feature map, yielding a semantic map. Average feature values are visualized in gray-scale heat maps and features relevant to aesthetic value are identified. We call this the Pixel Array Self Organizing Map (PASOM) method. For the set of images analyzed, brightness and texture features were identified as being discriminatory between images of high and low aesthetic value. High aesthetic value images tend to have higher brightness and richer textures. These findings were corroborated by a professional artist/photographer as being consistent with the principles for attaining aesthetic value in visual media. The PASOM method yields a semantic map and a visualization of feature value variation that together make possible a detailed analysis of features associated with the aesthetic value of images.