Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Video search re-ranking via multi-graph propagation
Proceedings of the 15th international conference on Multimedia
Reranking Methods for Visual Search
IEEE MultiMedia
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Learning tag relevance by neighbor voting for social image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Proceedings of the 18th international conference on World wide web
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Proceedings of the international conference on Multimedia
Improving social tag-based image retrieval with CBIR technique
ICADL'10 Proceedings of the role of digital libraries in a time of global change, and 12th international conference on Asia-Pacific digital libraries
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Social media sharing web sites like Flickr allow users to annotate images with free tags, which greatly facilitate social image search and browsing. However, currently tag-based image search on Flickr does not provide the option of relevance-based ranking, i.e., the search results cannot be ranked according to their relevance levels with respect to the query tag, and this has limited the effectiveness of tag-based search. In this paper, we propose a relevance-based ranking scheme for social image search, aiming to automatically rank images according to their relevance to the query tag. It integrates both the visual consistency between images and the semantic correlation between tags in a unified optimization framework. We propose an iterative method to solve the optimization problem, and the relevance-based ranking can thus be accomplished. Experimental results on real Flickr image collection demonstrate the effectiveness of the proposed approach.