Exploiting user information for image tag refinement

Photo sharing websites allow users to describe images with freely chosen tags. The user-generated tags not only facilitate the users in sharing and organizing images, but also provide large scale meaningful data for image retrieval and management. Extensive studies on improving the quality of user-generated tags for tag-based applications focused on exploiting the image-tag, image-image and tag-tag binary relationships. Considering that user is the originator of the tagging activity and user involves with image and tag in many aspects, in this paper we tackle the problem of tag refinement by leveraging user information. We propose a Tensor Decomposition framework to jointly model the ternary user-image-tag interrelation and respective intra-relations. The users, images and tags are represented in the corresponding latent subspaces. For a given image, the tags with the highest cross-space associations are reserved as the final annotation. The proposed method is validated on a large-scale real-world dataset.