When we look at a fine art painting, we can infer information about its style (e.g. Baroque vs. Impressionism), genre (e.g. a portrait or a landscape), and even the artist who painted it. This impressive ability of human perception for learning and judging complex aesthetic-related visual concepts has long been thought to not be a logical process. In our research, however, we tackle this problem using a computational methodology, to show that machines can in fact learn such aesthetic-related concepts.