Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
ICML '05 Proceedings of the 22nd international conference on Machine learning
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Computational Biology and Chemistry
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Graph construction and b-matching for semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Geometry-aware metric learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Fast Similarity Search for Learned Metrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting structures in image collections for object recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Adapting visual category models to new domains
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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Graph-based methods are very popular in semi-supervised learning due to their well founded theoretical background, intuitive interpretation of local neighborhood structure, and strong performance on a wide range of challenging learning problems. However, the success of these methods is highly dependent on the pre-existing neighborhood structure in the data used to construct the graph. In this paper, we use metric learning to improve this critical step by increasing the precision of the nearest neighbors and building our graph in this new metric space. We show that learning of neighborhood relations before constructing the graph consistently improves performance of two label propagation schemes on three different datasets - achieving the best performance reported on Caltech 101 to date. Furthermore, we question the predominant random draw of labels and advocate the importance of the choice of labeled examples. Orthogonal to active learning schemes, we investigate how domain knowledge can substantially increase performance in these semi-supervised learning settings.