Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Multi-view multi-label active learning for image classification
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Improving image annotation in imbalanced classification problems with ranking SVM
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
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Most photo sharing sites give their users the opportunity to manually label images. The labels collected that way are usually very incomplete due to the size of the image collections: most images are not labeled according to all the categories they belong to, and, conversely, many class have relatively few representative examples. Automated image systems that can deal with small amounts of labeled examples and unbalanced classes are thus necessary to better organize and annotate images. In this work, we propose a multiview semi-supervised bipartite ranking model which allows to leverage the information contained in unlabeled sets of images in order to improve the prediction performance, using multiple descriptions, or views of images. For each topic class, our approach first learns as many view-specific rankers as available views using the labeled data only. These rankers are then improved iteratively by adding pseudo-labeled pairs of examples on which all view-specific rankers agree over the ranking of examples within these pairs. We report on experiments carried out on the NUS-WIDE dataset, which show that the multiview ranking process improves predictive performances when a small number of labeled examples is available specially for unbalanced classes. We show also that our approach achieves significant improvements over a state-of-the art semi-supervised multiview classification model.