Learning multiple local binary descriptors for image matching

Binary descriptors have received extensive research interests due to their low memory storage and computational efficiency. However, the discriminative ability of the binary descriptors is often limited in comparison with general floating point ones. In this paper, we present a learning framework to effectively integrate multiple binary descriptors, which is referred as learning-based multiple binary descriptors (LMBD). We observe that previous successful binary descriptors like Receptive Fields Descriptor (RFD) which includes rectangular pooling area (RFDR) and Gaussian pooling area (RFDG)), BinBoost, and Boosted Gradient Maps (BGM), are highly complementary to each other. We show that the proposed LMBD can improve the discriminative ability of individual binary descriptors significantly. We formulate the fusion of multiple groups of the binary descriptors was formulated as a pair-wise ranking problem, which can be solved effectively in a rankSVM framework. Extensive experiments were conducted to evaluate the efficiency of LMBD. The proposed LMBD obtains the error rate of 12.44% on the challenging local patch datasets, which is about 2% lower than the state-of-the-art results (obtained by a learning based floating point descriptor). Furthermore, the proposed binary descriptor also outperforms other binary descriptors on image matching task.