Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Signals & systems (2nd ed.)
Adjustment Learning and Relevant Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Illumination insensitive recognition using eigenspaces
Computer Vision and Image Understanding
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
Correlation Pattern Recognition
Correlation Pattern Recognition
Nonparametric Discriminant Analysis for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
| Hi-index | 0.01 |
It is widely understood that the performance of the nearest neighbor (NN) rule is dependent on: (i) the way distances are computed between different examples, and (ii) the type of feature representation used. Linear filters are often used in computer vision as a pre-processing step, to extract useful feature representations. In this paper we demonstrate an equivalence between (i) and (ii) for NN tasks involving weighted Euclidean distances. Specifically, we demonstrate how the application of a bank of linear filters can be re-interpreted, in the form of a symmetric weighting matrix, as a manipulation of how distances are computed between different examples for NN classification. Further, we argue that filters fulfill the role of encoding local spatial constraints into this weighting matrix. We then demonstrate how these constraints can dramatically increase the generalization capability of canonical distance metric learning techniques in the presence of unseen illumination and viewpoint change.