Photo filter recommendation through analyzing objects, scenes and aesthetics

Photo filters are widespread - they give photos a stylized look without requiring the user to have professional knowledge of image processing. However, with the increasing number of photo filters and limited display size of mobile devices, selecting a proper filter for a given photo can be a tedious task for camera phone users. In this paper, we present a photo filter recommendation approach to address this problem. In particular, we rely on state-of-the-art deep learning approaches to extract objects, scenes, and image aesthetics reliably and represent them using deep features as the observations of our recommendation model. Furthermore, we collect 68,400 filtered photos from Instagram to learn the relationships among objects, scenes, aesthetics, and filter types. Experimental results using the FACD benchmark dataset demonstrate the state-of-the-art recommendation performance of the proposed approach; these results show that objects, scenes, and aesthetic attributes influence filter preference.