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Matching keypoint pairs of different images is a basic task of computervision. Most methods require customized extremum point schemes to obtain thecoordinates of feature points with high confidence, which often need complexalgorithmic design or a network with higher training difficulty and also ignorethe possibility that flat regions can be used as candidate regions of matchingpoints. In this paper, we design a region-based descriptor by combining thecontext features of a deep network. The new descriptor can give a robustrepresentation of a point even in flat regions. By the new descriptor, we canobtain more high confidence matching points without extremum operation. Theexperimental results show that our proposed method achieves a performancecomparable to state-of-the-art.

A region-based descriptor network for uniformly sampled keypoints