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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
An efficient parts-based near-duplicate and sub-image retrieval system
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CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Inverted files for text search engines
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AnnoSearch: Image Auto-Annotation by Search
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LabelMe: A Database and Web-Based Tool for Image Annotation
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ACM Computing Surveys (CSUR)
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-Scale Discovery of Spatially Related Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corpus-based semantic class mining: distributional vs. pattern-based approaches
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Partition min-hash for partial duplicate image discovery
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
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ACM Transactions on Intelligent Systems and Technology (TIST)
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CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image search—from thousands to billions in 20 years
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
Picture tags and world knowledge: learning tag relations from visual semantic sources
Proceedings of the 21st ACM international conference on Multimedia
Image context discovery from socially curated contents
Proceedings of the 21st ACM international conference on Multimedia
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Even after 20 years of research on real-world image retrieval, there is still a big gap between what search engines can provide and what users expect to see. To bridge this gap, we present an image knowledge base, ImageKB, a graph representation of structured entities, categories, and representative images, as a new basis for practical image indexing and search. ImageKB is automatically constructed via a both bottom-up and top-down, scalable approach that efficiently matches 2 billion web images onto an ontology with millions of nodes. Our approach consists of identifying duplicate image clusters from billions of images, obtaining a candidate set of entities and their images, discovering definitive texts to represent an image and identifying representative images for an entity. To date, ImageKB contains 235.3M representative images corresponding to 0.52M entities, much larger than the state-of-the-art alternative ImageNet that contains 14.2M images for 0.02M synsets. Compared to existing image databases, ImageKB reflects the distributions of both images on the web and users' interests, contains rich semantic descriptions for images and entities, and can be widely used for both text to image search and image to text understanding.