GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Graph-based semi-supervised learning with multiple labels
Journal of Visual Communication and Image Representation
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Unified tag analysis with multi-edge graph
Proceedings of the international conference on Multimedia
Image tag refinement towards low-rank, content-tag prior and error sparsity
Proceedings of the international conference on Multimedia
Semantic context learning with large-scale weakly-labeled image set
Proceedings of the 21st ACM international conference on Information and knowledge management
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It is very attractive to exploit weakly-labeled image dataset for multi-label annotation applications. In our paper the meaning of the terminology weakly labeled is threefold: i) only a small subset of the available images are labeled; ii) even for the labeled image, the given labels may be uncorrect or incomplete; iii) the given labels do not provide the exact object locations in the images. A novel method is developed to predict the multiple labels for images and to provide region-level labels for the objects. We cluster the image regions to learn several region-exemplars and predict the label vector for each image region as a locally weighted average of the label vectors on exemplars. By investigating the label confidence matrix for the region-exemplars from different perspectives (column picture and row picture), we sufficiently leverage the visual contexts, the semantic contexts, and the consistency between similarities in the visual feature space and semantic label space. Experimental results on real web images demonstrate the effectiveness of the proposed method.