Ontology driven content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Image clustering based on a shared nearest neighbors approach for tagged collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Visual diversification of image search results
Proceedings of the 18th international conference on World wide web
Constructing folksonomies from user-specified relations on flickr
Proceedings of the 18th international conference on World wide web
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Compressing tags to find interesting media groups
Proceedings of the 18th ACM conference on Information and knowledge management
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
Multimodal feature generation framework for semantic image classification
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Tag completion based on belief theory and neighbor voting
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Div400: a social image retrieval result diversification dataset
Proceedings of the 5th ACM Multimedia Systems Conference
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People often try to find an image using a short query and images are usually indexed using short annotations. Matching the query vocabulary with the indexing vocabulary is a difficult problem when little text is available. Textual user generated content in Web 2.0 platforms contains a wealth of data that can help solve this problem. Here we describe how to use Wikipedia and Flickr content to improve this match. The initial query is launched in Flickr and we create a query model based on co-occurring terms. We also calculate nearby concepts using Wikipedia and use these to expand the query. The final results are obtained by ranking the results for the expanded query using the similarity between their annotation and the Flickr model. Evaluation of these expansion and ranking techniques, over the Image CLEF 2010 Wikipedia Collection containing 237,434 images and their multilingual textual annotations, shows that a consistent improvement compared to state of the art methods.