Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
MiAlbum - a system for home photo managemet using the semi-automatic image annotation approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Leveraging context to resolve identity in photo albums
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
An efficient manual image annotation approach based on tagging and browsing
Workshop on multimedia information retrieval on The many faces of multimedia semantics
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Automatic image semantic interpretation using social action and tagging data
Multimedia Tools and Applications
The InfoAlbum image centric information collection
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Tag recommendation for georeferenced photos
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
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In this paper, we propose an automatic photo tag expansion system for the community photo collections, such as Flickr. Our aim is to suggest relevant tags for a target photograph uploaded to the system by a user, by incorporating the visual and textual cues from other related photographs. As the first step, the system requires the user to add only a few initial tags for each uploaded photo. These initial tags are used to retrieve related photos including the same tags in their tag lists. Then the set of candidate tags collected from a large pool of photos is weighted according to the similarity of the target photo to the retrieved photo including the tag. Finally, the tags in the highest rankings are used to automatically expand the tags of the target photo. The experimental results on Flickr photos show that, the use of visual similarity of semantically relevant photos to recommend tags improves the quality of suggested tags compared to only text-based systems.