Images can vary according to changes in viewpoint, resolution, noise, and illumination. In this paper, we aim to learn representations for an image, which are robust to wide changes in such environmental conditions, using training pairs of matching and non-matching local image patches that are collected under various environmental conditions. We present a regularized discriminant analysis that emphasizes two challenging categories among the given training pairs: (1) matching, but far apart pairs and (2) non-matching, but close pairs in the original feature space (e.g., SIFT feature space). Compared to existing work on metric learning and discriminant analysis, our method can better distinguish relevant images from irrelevant, but look-alike images.
@article{arxiv.1301.3644,
title = {Regularized Discriminant Embedding for Visual Descriptor Learning},
author = {Kye-Hyeon Kim and Rui Cai and Lei Zhang and Seungjin Choi},
journal= {arXiv preprint arXiv:1301.3644},
year = {2013}
}
Comments
3 pages + 1 additional page containing only cited references; The full version of this manuscript is currently under review in an international journal