Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
Image annotation refinement using random walk with restarts
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Proceedings of the 18th international conference on World wide web
Tag refinement by regularized LDA
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Unified tag analysis with multi-edge graph
Proceedings of the international conference on Multimedia
Image tag refinement towards low-rank, content-tag prior and error sparsity
Proceedings of the international conference on Multimedia
Proceedings of the international conference on Multimedia
Image annotation using multi-correlation probabilistic matrix factorization
Proceedings of the international conference on Multimedia
Content-based tag processing for Internet social images
Multimedia Tools and Applications
Social-oriented visual image search
Computer Vision and Image Understanding
| Hi-index | 0.00 |
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.