目的 通过挖掘图像局部区域特征信息，提出了一种鲁棒性更高的二进制描述子。针对BRIEF（binary robust independent elementary features）关于旋转和视角变化鲁棒性差的问题，通过图像补丁分层处理、增加关键点图像补丁个数来捕获更多的局部特征信息，对BRIEF描述子改进。方法 首先，根据灰度序列对补丁内所有像素点分类，像素的一个聚类形成了一个亚补丁，然后在每个亚补丁上进行类似BRIEF的随机测试。其次，由于原图像补丁大小、尺度大小影响补丁的像素点成分，从而影响像素聚类的效果，所以在原图像关键点周围分割出多个不同大小的图像补丁，或是将原图像补丁根据尺度金字塔确定几个尺度大小不同的补丁，然后再对图像补丁进行分层、测试。所构建的描述子不仅包含了补丁像素的灰度比较信息，而且包含了灰度排序信息和像素群聚信息，提高了描述子的鲁棒性。结果 通过性能对比实验，发现所提的描述子的性能提高了，而且好于对比的浮点描述子。结论 挖掘图像补丁的特征信息能提高二进制描述子的鲁棒性。

Objective Since using limited spatial information from only one image patch to structure conventional binary descriptor such as BRIEF will result in low discriminative ability and is invariant to rotation or viewpoint changes. We work on this problem through further excavating feature information of the region of interest and by proposing a binary descriptor encoding, which does not only include intensity-comparison information but also information about relative relationship of intensities. Method This can be done by taking BRIEF-like tests on image patches gotten in two ways. First, several sub-patches can be obtained by decomposing one original image patch by sorting all pixels on it according to their intensity order, then random tests are used on each sub-patch, and all test results are concatenated to form a distinct binary string of sub-patches; second, multiple original image patches are produced by dividing interest regions into several patches of different sizes around the keypoint, or they can be retrieved from the scale space of image patches, and the discriminative power of the descriptor could be further raised by taking tests on these multiple patches. Result Results based on experiments of performance evaluation have shown that the proposed binary descriptor outperforms other binary descriptors or even floating point descriptors under various image transformations. Conclusion Excavating feature information in local image region can improve the robustness of binary descriptors.