The Architecture of Cognition
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
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
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Inferring semantic concepts from community-contributed images and noisy tags
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
Proceedings of the ACM International Conference on Image and Video Retrieval
Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images
ACM Transactions on Intelligent Systems and Technology (TIST)
A two-view learning approach for image tag ranking
Proceedings of the fourth ACM international conference on Web search and data mining
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In this paper, we explore the problem of tag ranking by propagating relevance over community-contributed images and their associated tags. To rank the tags more accurately, we propose a novel tag ranking scheme through a two-stage graph-based relevance propagation approach. The first stage constructs a tag graph on each image and implements a random walk process on it in order to get the initial relevance of each tag for one image and the second stage builds a kNN-sparse image graph and propagates the relevance of tags among the web images. The proposed approach is purely data-driven, since the explicit relevance models between tags and images are not assumed. More importantly, compared to existing tag ranking approaches, we propose to leverage the relevance propagation over two graphs, which take into count not only the relationship among tags but also the relationship among images. Extensive experiments have conducted on the NUS-WIDE dataset have demonstrated the effectiveness of the proposed approach.