Computational Aesthetics and Visual Preference - An Experimental Approach

Computational Aesthetics is a term which has been frequently used by scientists interested in quantitative approaches towards the concept of aesthetics during the last century. A review of both past and recent works attempting to quantify aesthetic preference of various stimuli is given, which shows that aesthetics was described as some function of complexity and order in many theories. Since most measures were hardly relating to knowledge of human perception, complexity is reinterpreted in the context of a currently accepted model of visual prerception and a hypothesis is formulated which states that human visual preference is not independent of complexity (cell excitation) at the very first stage of visual processing. An estimate representative for cell activity in early visual processing is presented: Multivariate Gabor Filter Responses. Additionally, image properties such as aspect ratio, resolution and JPEG compressibility are used to sanity-check any correlations. The estimate calculated, compared against human preference ratings of photographs, shows statistically significant but low correlations. However, the machine learning experiments performed, fail to predict any better than one would by taking the mean value of the data. Even though these results only loosely relate to aesthetic perception, it´s motivating furhter research and closer inspection of image features and their relation to perceptual properties and visual (aesthetic) preference.