Flickr circles: Mining socially-aware aesthetic tendency

Abstract

Aesthetic tendency discovery is a useful and interesting application in social media. This paper proposes to categorize large-scale Flickr users into multiple circles. Each circle contains users with similar aesthetic interests (e.g., landscapes or abstract paintings). We notice that: (1) an aesthetic model should be flexible as different visual features may be used to describe different image sets, and (2) the numbers of photos from different users varies significantly and some users have very few photos. Therefore, a regularized topic model is proposed to quantify user's aesthetic interest as a distribution in the latent space. Then, a graph is built to describe the similarity of aesthetic interests among users. Obviously, densely connected users are with similar aesthetic interests. Thus an efficient dense subgraph mining algorithm is adopted to group users into different circles. Experiments show that our approach accurately detects circles on an image set crawled from over 60,000 Flickr users.