The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Visual diversification of image search results
Proceedings of the 18th international conference on World wide web
Exploiting Flickr Tags and Groups for Finding Landmark Photos
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Exploiting Wikipedia as external knowledge for document clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing cluster labeling using wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Mining city landmarks from blogs by graph modeling
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
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Wikipedia, as an open editable resource, provides reliable knowledge and taxonomy. Contrast to the rich literal information, Wikipedia is lack of visual illustrations, like images and animations. Can we visually annotate Wikipedia concept and provide representative images according to its taxonomy? The huge amount of online social media, such as the tagged images in Flickr, is a good visual resource. Nevertheless, the noisy nature of the tags hinders itself. Based on the observation that images are often collected by the groups with common interest or topic, we propose a framework to visually annotate Wikipedia via social community. The contribution of our work is two-fold: (i) we diversely enrich Wikipedia with images based on its taxonomy; (ii) we introduce community effort to overcome the noisy nature of tags in harvesting images. This work shows our concept and community data collection of the proposed system.