  Matching keypoint pairs of different images is a basic task of computer
vision. Most methods require customized extremum point schemes to obtain the
coordinates of feature points with high confidence, which often need complex
algorithmic design or a network with higher training difficulty and also ignore
the possibility that flat regions can be used as candidate regions of matching
points. In this paper, we design a region-based descriptor by combining the
context features of a deep network. The new descriptor can give a robust
representation of a point even in flat regions. By the new descriptor, we can
obtain more high confidence matching points without extremum operation. The
experimental results show that our proposed method achieves a performance
comparable to state-of-the-art.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing