GDBID: fusion gradient distinction binary image descriptor

In recent years, binary descriptors have attracted more and more attention due to their low memory consumption and high speed. It is well known that these representations are worse than higher-dimensional and histogram-based descriptors such as SIFT. Therefore, this paper proposes a fusion gradient distinction binary image descriptor (GDBID). Gradient comparison is added on the basis of the original gray comparison to enrich the information contained in the descriptor. At the same time, the comparison patches of different sizes are obtained by constructing concentric circles to achieve anti-noise. In addition, a threshold is set to filter patches to reduce the dimension of descriptors. Experimental results show that the GDBID has a precision is close to the best algorithm (SIFT), and the time consumption is lower than the fastest ORB in the literature.