RWBD: Learning Robust Weighted Binary Descriptor for Image Matching

Binary descriptors are drawing increasing interest as they enable faster processing in a lower memory footprint, which are suitable to large-scale and real-time vision applications. However, the performance of most existing binary descriptors tends to be inferior to those of the competing floating-point descriptors. In this paper, by considering each bit might contribute differently to the robustness and distinctiveness, a novel binary descriptor based on binary weights learning is proposed, referred to as a robust weighted binary descriptor (RWBD). Technically, each bit of RWBD is built upon difference tests of pairwise complementary information sampling with varying Gaussian kernels, leading to a more informative and robust description. Furthermore, RWBD imposes different emphases on all bits utilizing the learned binary weights, which can significantly improve the discriminative power and preserve the fast matching advantage. Extensive experiments on the publicly challenging benchmarks, including image matching and object recognition data sets, clearly demonstrate the effectiveness and efficiency of the proposed approach.