Abstract Learning compact hash codes has been a vibrant research topic for large-scale similarity search owing to the low storage cost and expedited search operation. A recent research thrust aims to learn compact codes jointly from multiple sources, referred to as cross-view (or cross-modal) hashing in the literature. The main theme of this paper is to develop a novel formulation and optimization scheme for cross-view hashing. As a key differentiator, our proposed method directly conducts optimization on discrete binary hash codes, rather than relaxed continuous variables as in existing cross-view hashing methods. This way relaxation-induced search accuracy loss can be avoided. We attack the crossview hashing problem by simultaneously capturing semantic neighboring relations and maximizing the generative probability of the learned hash codes in each view. Specififically, to enable effective optimization on discrete hash codes, the optimization proceeds in a block coordinate descent fashion. Each iteration sequentially updates a single bit with others clamped. We transform the resultant sub-problem into an equivalent, more tractable quadratic form and devise an active set based solver on the discrete codes. Rigorous theoretical analysis is provided for the convergence and local optimality condition. Comprehensive evaluations are conducted on three image benchmarks. The clearly superior experimental results faithfully prove the merits of the proposed method.