  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.
