GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Classification with partial labels
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Multi-label learning with incomplete class assignments
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
| Hi-index | 0.00 |
In this paper, we address the problem of image annotation when the given labels of training image are incomplete, inaccurate, and unevenly distributed, in the form of weak labels, which is frequently encountered when dealing with large scale web image training set. We introduce a progressive semantic neighborhood learning approach that explicitly addresses the challenge of learning from weakly labeled image by searching image's semantic consistent neighborhood. Neighbors in image's semantic consistent neighborhood have global similarity, partial correlation, conceptual similarity along with semantic balance. We also present an efficient label inference algorithm to handle noise by minimizing the neighborhood reconstruction error. Experiments with different data sets show that the proposed framework is more effective than the state-of-the-art algorithms in dealing with weakly labeled datasets.