Bridging the Aesthetic Gap: The Wild Beauty of Web Imagery.

To provide good results, image search engines need to rank not just the most relevant images, but also the highest quality images. To surface beautiful pictures, existing computational aesthetic models are trained with datasets from photo contest websites, dominated by professional photos. Such models fail completely in real web scenarios, where images are extremely diverse in terms of quality and type (e.g. drawings, clip-art, etc). This work aims at bridging and understanding this \"aesthetic gap\". We collect a dataset of around 100K web images with `quality' and `type' (photo vs non-photo) annotations. We design a set of visual features to describe image pictorial characteristics, and deeply analyse the peculiar beauty of web images as opposed to appealing professional images. Finally, we build a set of computational aesthetic frameworks based on deep learning and hand-crafted features that take into account the diverse quality of web images, and show that they significantly outperform traditional computational aesthetics methods on our dataset.