Due to the advantages of low storage cost and high retrieval efficiency, cross-modal hashing has received considerate attention. Most existing deep cross-modal hashing adopt a symmetric strategy to learn same deep hash functions for both query instances and database instances. However, the training of these symmetric deep cross-modal hashing methods is time-consuming, which makes them hard to effectively utilize the supervised information for cases with large-scale datasets. Inspired by the latest advance in the asymmetric hashing scheme, in this paper, we propose a discriminative deep asymmetric supervised hashing (DDASH) for cross-modal retrieval. Specifically, asymmetric hashing only learns hash codes of query instances by deep hash functions while learning the hash codes of the database instances by hand-crafted matrices. It cannot only make full use of the information in large-scale datasets, but also reduce the training time. Besides, we introduce discrete optimization to reduce the binary quantization error. Furthermore, a mapping matrix which maps generated hash codes into the corresponding labels is introduced to ensure that the hash codes are discriminative. We also calculate the level of similarity between instances as supervised information. Experiments on three common datasets for cross-modal retrieval show that DDASH outperforms state-of-the-art cross-modal hashing methods.

由于存储成本低和检索效率高的优点，跨模式散列受到了广泛的关注。大多数现有的深层交叉模式哈希都采用对称策略来为查询实例和数据库实例学习相同的深层哈希函数。但是，这些对称的深层交叉模态哈希方法的训练很耗时，这使得它们难以有效地将监督信息用于具有大规模数据集的案例。受非对称哈希方案的最新进展启发，本文提出了一种可识别的深度非对称监督哈希（DDASH），用于跨模式检索。具体而言，非对称哈希仅通过深层哈希函数学习查询实例的哈希码，而通过手工矩阵学习数据库实例的哈希码。它不仅可以充分利用大规模数据集中的信息，而且可以减少训练时间。此外，我们引入离散优化以减少二进制量化误差。此外，引入了将生成的哈希码映射到相应标签中的映射矩阵，以确保哈希码是可区分的。我们还将实例之间的相似度计算为监督信息。在用于交叉模式检索的三个通用数据集上进行的实验表明，DDASH优于最新的交叉模式哈希方法。引入了将生成的哈希码映射到相应标签的映射矩阵，以确保哈希码是可区分的。我们还将实例之间的相似度计算为监督信息。在用于交叉模式检索的三个通用数据集上进行的实验表明，DDASH优于最新的交叉模式哈希方法。引入了将生成的哈希码映射到相应标签的映射矩阵，以确保哈希码是可区分的。我们还将实例之间的相似度计算为监督信息。在用于交叉模式检索的三个通用数据集上进行的实验表明，DDASH优于最新的交叉模式哈希方法。