Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
To search or to label?: predicting the performance of search-based automatic image classifiers
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Learning tag relevance by neighbor voting for social image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Proceedings of the 18th international conference on World wide web
Proceedings of the 18th international conference on World wide web
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Web-scale computer vision using MapReduce for multimedia data mining
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Unsupervised multi-feature tag relevance learning for social image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Rich location-driven tag cloud suggestions based on public, community, and personal sources
Proceedings of the 1st ACM international workshop on Connected multimedia
Tag suggestion and localization in user-generated videos based on social knowledge
Proceedings of second ACM SIGMM workshop on Social media
Automatic image semantic interpretation using social action and tagging data
Multimedia Tools and Applications
Content-based tag processing for Internet social images
Multimedia Tools and Applications
On the consistency and features of image similarity
Proceedings of the 4th Information Interaction in Context Symposium
Proceedings of the 2012 international workshop on Socially-aware multimedia
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This paper describes an approach for finding image descriptors or tags that are highly reliable and specific. Reliable, in this work, means that the tags are related to the image's visual content, which we verify by finding two or more real people who agree that the tag is applicable. Our work differs from prior work by mining the photographer's (or web master's) original words and seeking inter-subject agreement for images that we judge to be highly similar. By using the photographer's words we gain specificity since the photographer knows that the image represents something specific, such as the Augsburg Cathedral; whereas random people from the web playing a labeling game might not have this knowledge. We describe our approach and demonstrate that we identify reliable tags with greater specificity than human annotators.