  Deep supervised hashing for image retrieval has attracted researchers'
attention due to its high efficiency and superior retrieval performance. Most
existing deep supervised hashing works, which are based on pairwise/triplet
labels, suffer from the expensive computational cost and insufficient
utilization of the semantics information. Recently, deep classwise hashing
introduced a classwise loss supervised by class labels information
alternatively; however, we find it still has its drawback. In this paper, we
propose an improved deep classwise hashing, which enables hashing learning and
class centers learning simultaneously. Specifically, we design a two-step
strategy on center similarity learning. It interacts with the classwise loss to
attract the class center to concentrate on the intra-class samples while
pushing other class centers as far as possible. The centers similarity learning
contributes to generating more compact and discriminative hashing codes. We
conduct experiments on three benchmark datasets. It shows that the proposed
method effectively surpasses the original method and outperforms
state-of-the-art baselines under various commonly-used evaluation metrics for
image retrieval.
