A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Image searching on the Excite web search engine
Information Processing and Management: an International Journal
Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Information Processing and Management: an International Journal
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Active tagging for image indexing
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
How are we searching the World Wide Web? A comparison of nine search engine transaction logs
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Folksonomies. Indexing and Retrieval in Web 2.0
Folksonomies. Indexing and Retrieval in Web 2.0
Social image search with diverse relevance ranking
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Automatic tag generation and ranking for sensor-rich outdoor videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Reading between the tags to predict real-world size-class for visually depicted objects in images
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
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Social image sharing websites like Flickr have successfully motivated users around the world to annotate images with tags, which greatly facilitate search and organization of social image content. However, these manually-input tags are far from a comprehensive description of the image content, which limits effectiveness of the tags in content-based image search. In this paper, we propose an automatic scheme called tagging tags to supplement semantic image descriptions by associating a group of property tags with each existing tag. For example, an initial tag "tiger" will be further tagged with "white", "stripes" and "bottom-right" along three tag properties: color, texture and location, respectively. In the proposed scheme, a lazy learning approach is first applied to estimate the corresponding image regions of each initial tag, and then a set of property tags, which involve six exemplary property aspects including location, color, texture, shape, size and dominance, are derived for each tag according to the content of the regions and the entire image. These tag properties enable much more precise image search especially when certain tag properties are included in the query. The results of the empirical evaluation show that tag properties remarkably boost the performance of social image retrieval.