A Ranking Based Model for Automatic Image Annotation in a Social Network

We propose a relational ranking model for learning to tag images in social media sharing systems. This model learns to associate a ranked list of tags to unlabeled images, by considering simultaneously content information (visual or textual) and relational information among the images. It is able to handle implicit relations like content similarities, and explicit ones like friendship or authorship. It can be used either for fully automatic image labeling or for helping the user with a ranked list of candidate tags. The model itself is based on a transductive algorithm thats learns from both labeled and unlabeled data. Experiments on a real corpus extracted from Flickr show the effectiveness of this model, particularly when using authorship and friendship relations.