  Recently, deep supervised hashing methods have become popular for large-scale
image retrieval task. To preserve the semantic similarity notion between
examples, they typically utilize the pairwise supervision or the triplet
supervised information for hash learning. However, these methods usually ignore
the semantic class information which can help the improvement of the semantic
discriminative ability of hash codes. In this paper, we propose a novel
hierarchy neighborhood discriminative hashing method. Specifically, we
construct a bipartite graph to build coarse semantic neighbourhood relationship
between the sub-class feature centers and the embeddings features. Moreover, we
utilize the pairwise supervised information to construct the fined semantic
neighbourhood relationship between embeddings features. Finally, we propose a
hierarchy neighborhood discriminative hashing loss to unify the single-label
and multilabel image retrieval problem with a one-stream deep neural network
architecture. Experimental results on two largescale datasets demonstrate that
the proposed method can outperform the state-of-the-art hashing methods.
