Mining personal image collection for social group suggestion

Popular photo-sharing sites have attracted millions of people and helped construct massive social networks in cyberspace. Different from traditional social relationship, users actively interact within groups where common interests are shared on certain types of events or topics captured by photos and videos. Contributing images to a group would greatly promote the interactions between users and expand their social networks. In this work, we intend to produce accurate predictions of suitable photo-sharing groups from a user's images by mining images both on the Web and in the user's personal collection. To this end, we designed a new approach to cluster popular groups into categories by analyzing the similarity of groups via SimRank. Both visual content and its annotations are integrated to understand the events or topics depicted in the images. Experiments on real user images demonstrate the feasibility of the proposed approach.