  Local feature provides compact and invariant image representation for various
visual tasks. Current deep learning-based local feature algorithms always
utilize convolution neural network (CNN) architecture with limited receptive
field. Besides, even with high-performance GPU devices, the computational
efficiency of local features cannot be satisfactory. In this paper, we tackle
such problems by proposing a CNN-based local feature algorithm. The proposed
method introduces a global enhancement module to fuse global visual clues in a
light-weight network, and then optimizes the network by novel deep
reinforcement learning scheme from the perspective of local feature matching
task. Experiments on the public benchmarks demonstrate that the proposal can
achieve considerable robustness against visual interference and meanwhile run
in real time.
