Learning a Similarity Metric Discriminatively, with Application to Face Verification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Hi-index | 0.00 |
The k-nearest neighbor (KNN) classifier is a simple and effective method for image classification. However, its performance significantly depends on how the distance between samples is calculated. Therefore, learning an appropriate distance metric is the most important issue for the KNN-based classifiers. The distance metric can be learned from either labeled or unlabeled data. Labeled images are expensive to generate, while unlabeled images are abundant, and the label information is crucial for the performance of the learned metric. In this work, we present a semi-supervised method for learning the distance metric. We propose a semi-supervised extension to the Neighborhood Component Analysis (NCA) method, which is a supervised method especially tailored for KNN classifiers. Then, we use the learned distance metric to classify images using the KNN method. Experiment shows that our proposed method outperforms both the traditional supervised and unsupervised methods.