SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Amsterdam Library of Object Images
International Journal of Computer Vision
Enhancing image annotation by integrating concept ontology and text-based bayesian learning model
Proceedings of the 15th international conference on Multimedia
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
RankCompete: simultaneous ranking and clustering of web photos
Proceedings of the 19th international conference on World wide web
Laplacian Support Vector Machines Trained in the Primal
The Journal of Machine Learning Research
A correlation approach for automatic image annotation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Annotation propagation in image databases using similarity graphs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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The aim of this paper is to reduce to a minimum the level of human intervention in the semantic annotation process of images. Ideally, only one copy of each object of interest would be labeled manually, and the labels would then be propagated automatically to all other occurrences of the objects in the database. To that end, we propose a neighbor-based influence propagation approach KProp which builds a voting model and propagates the knowledge associated to some objects to similar objects. We show that KProp can perform efficiently through matrix computations and achieve better performance with fewer labeled examples per object.