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Classification of photographic images based on perceived aesthetic quality

In this paper, we explore automated aesthetic evaluation of photographs using machine learning and image processing techniques. We theorize that the spatial distribution of certain visual elements within an image correlates with its aesthetic quality. To this end, we present a novel approach wherein we model each photograph as a set of image tiles, extract visual features from each tile, and train a classifier on the resulting features along with the images’ aesthetics ratings. Our model achieves a 10fold cross-validation classification success rate of 85.03%, corroborating the efficacy of our methodology and therefore showing promise for future development.