Tell Me What You Like and I'll Tell You What You Are: Discriminating Visual Preferences on Flickr Data

The John Ruskin's 19th century adage suggests that personal taste is not merely an absolute set of aesthetic principles valid for everyone: actually, it is a process of interpretation which have also roots in one's life experiences. This aspect represents nowadays a major problem for inferring automatically the quality of a picture. In this paper, instead of trying to solve this age-old problem, we consider an intriguing, orthogonal direction, aimed at discovering how different are the personal tastes. Given a set of preferred images of a user, obtained from Flickr, we extract a pool of low- and high-level features; LASSO regression is then exploited to learn the most discriminative ones, considering a group of 200 random Flickr users. Such aspects can be easily recovered, allowing to understand what is the "what we like" which distinguish us from the others. We then perform multi-class classification, where a test sample is a set of preferred pictures of an unknown user, and the class...