Rating Image Aesthetics Using a Crowd Sourcing Approach

Any system that is able to reliably measure the aesthetic appeal of photographs would be of considerable importance to the digital imaging industry. Researchers have built automated rating systems using machine learning techniques applied to features extracted from images. In this paper, we study the effectiveness of ACQUINE, a comprehensive and publicly available rating system, using data obtained from voters in a crowd sourced manner. We analyze the effect of voting using a simple binary like/dislike rating in comparison to a numerical 10 point scale. We also show that global measures of image quality, such as contrast or colorfulness, do not correlate well with human ratings. The role of composition in determining human rating of aesthetics is discussed.