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. 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. 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. Excavating feature information in local image region can improve the robustness of binary descriptors.