WordNet: a lexical database for English
Communications of the ACM
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Formulating Semantic Image Annotation as a Supervised Learning Problem
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
Graph based multi-modality learning
Proceedings of the 13th annual ACM international conference on Multimedia
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multi-way clustering using super-symmetric non-negative tensor factorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Exploring hypergraph-based semi-supervised ranking for query-oriented summarization
Information Sciences: an International Journal
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Automatic image annotation is fundamental for effective image browsing and search. With the increasing size of image collections such as web images, it is infeasible to manually label large numbers of images. Meanwhile, the textual information contained in the hosting web pages can be used as approximate image description. However, such information is not accurate enough. In this paper, we propose a framework to utilize the visual content, the textual context, and the semantic relations between keywords to refine the image annotation. The hypergraph is used to model the textual information and the semantic relation is deduced by WordNet. Experiments on large-scale dataset demonstrate the effectiveness of the proposed method.