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Large-Scale Concept Ontology for Multimedia
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Why we tag: motivations for annotation in mobile and online media
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Towards automatic extraction of event and place semantics from flickr tags
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Proceedings of the 15th international conference on Multimedia
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Proceedings of the 17th ACM conference on Information and knowledge management
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WSMC '09 Proceedings of the 1st workshop on Web-scale multimedia corpus
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
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Proceedings of the 20th international conference on World wide web
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A Tree-Based Context Model for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring Networks of Diffusion and Influence
ACM Transactions on Knowledge Discovery from Data (TKDD)
Understanding web images by object relation network
Proceedings of the 21st international conference on World Wide Web
Visual and semantic similarity in ImageNet
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
WSABIE: scaling up to large vocabulary image annotation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
IEEE Transactions on Pattern Analysis and Machine Intelligence
Understanding and predicting importance in images
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Towards indexing representative images on the web
Proceedings of the 20th ACM international conference on Multimedia
Towards measuring the visualness of a concept
Proceedings of the 21st ACM international conference on Information and knowledge management
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This paper studies the use of everyday words to describe images. The common saying has it that 'a picture is worth a thousand words', here we ask which thousand? The proliferation of tagged social multimedia data presents a challenge to understanding collective tag-use at large scale -- one can ask if patterns from photo tags help understand tag-tag relations, and how it can be leveraged to improve visual search and recognition. We propose a new method to jointly analyze three distinct visual knowledge resources: Flickr, ImageNet/WordNet, and ConceptNet. This allows us to quantify the visual relevance of both tags learn their relationships. We propose a novel network estimation algorithm, Inverse Concept Rank, to infer incomplete tag relationships. We then design an algorithm for image annotation that takes into account both image and tag features. We analyze over 5 million photos with over 20,000 visual tags. The statistics from this collection leads to good results for image tagging, relationship estimation, and generalizing to unseen tags. This is a first step in analyzing picture tags and everyday semantic knowledge. Potential other applications include generating natural language descriptions of pictures, as well as validating and supplementing knowledge databases.