Binary hashing has been increasingly popular for efficient similarity search in large-scale vision problems. This paper presents a novel Supervised Discriminative Hashing (SDH) method by jointly modeling the global and local manifold structures. Specifically, a family of discriminative hash functions is designed to map data points of the original highdimensional space into nearby compact binary codes while preserving the geometrical similarity and discriminant properties in both global and local neighborhoods. Furthermore, the quantization loss between the original data and the binary codes together with the even binary code distribution are also taken into account in the optimization to generate more efficient and compact binary codes. Experimental results have demonstrated the proposed method outperforms the state-of-the-art.