Query refinement for multimedia similarity retrieval in MARS
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Web Image Retrieval Re-Ranking with Relevance Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Can all tags be used for search?
Proceedings of the 17th ACM conference on Information and knowledge management
Query Reformulation for Content Based Multimedia Retrieval in MARS
ICMCS '99 Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Latent visual context analysis for image re-ranking
Proceedings of the ACM International Conference on Image and Video Retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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With the recent rapid growth of social image hosting websites, such as Flickr, it is easier to construct a large database with tagged images. Social tags have been proven to be effective for providing keyword-based image retrieval and widely used on these websites, but whether they are beneficial for improving content-based image retrieval has not been well investigated in previous work. In this paper, we investigate whether and how social tags can be used for improving content-based image search results. We propose an unsupervised approach for automatic ranking without user interactions. It propagates visual and textual information on an image-tag relationship graph with a mutual reinforcement process. We conduct experiments showing that our approach can successfully use social tags for ranking and improving content-based social image search results, and performs better than other approaches.