In the feature matching problem, local keypoint representations are often not sufficiently
distinctive to disambiguate repetitive textures. State-of-the-art matching
pipelines encode global information and embed context into keypoint descriptors
to resolve this issue. In this thesis, we evaluate the failure modes of the state-ofthe-
art method for image matching. We identify the problem that including global
context to keypoint representations can sometimes eliminate their distinctiveness.
We propose to enhance the learning of the state-of-the-art pipeline by adding a metric
learning component to its objective function. By learning more distinctive global
context-aware keypoint descriptors, we recover the filtered matches without the loss
in matching precision.
