Towards category-based aesthetic models of photographs

We present a novel data-driven category-based approach to automatically assess the aesthetic appeal of photographs. In order to tackle this problem, a novel set of image segmentation methods based on feature contrast are introduced, such that luminance , sharpness , saliency , color chroma , and a measure of region relevance are computed to generate different image partitions. Image aesthetic features are computed on these regions (e.g. sharpness , colorfulness , and a novel set of light exposure features). In addition, color harmony , image simplicity , and a novel set of image composition features are measured on the overall image. Support Vector Regression models are generated for each of 7 popular image categories: animals , architecture , cityscape , floral , landscape , portraiture and seascapes . These models are analyzed to understand which features have greater influence in each of those categories, and how they perform with respect to a generic state of the art model.