Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 18th international conference on World wide web
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
A content-based method to enhance tag recommendation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
Image tag refinement towards low-rank, content-tag prior and error sparsity
Proceedings of the international conference on Multimedia
CASIS: a system for concept-aware social image search
Proceedings of the 21st international conference companion on World Wide Web
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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Tags associated with social images are valuable information source for superior image search and retrieval experiences. In this paper, we propose a novel tag recommendation technique that exploits the user-given tags associated with images. Each candidate tag to be recommended is described by a few tag concepts derived from the collective knowledge embedded in the tag co-occurrence pairs. Each concept, represented by a few tags with high co-occurrences among themselves, is indexed as a textual document. Then user-given tags of an image is represented as a text query and the matching concepts are retrieved from the index. The candidate tags associated with the matching concepts are then recommended. Leverages on the well studied Information Retrieval (IR) techniques, the proposed approach leads to superior tag recommendation accuracy and lower execution time compared to the state-of-the-art.