To date, large amounts of discriminative semantics-preserving discrete hash models are enjoying great popularity in cross-modal hashing community. Most of them, however, distill the shared hash codes from semantic tags, which ignores the intrinsic structure of features. Therefore, this paper proposes a direct extraction of discrete hash representation framework by factorizing latently similar structures for cross-modal retrieval in an unsupervised manner, which we refer to as Latent Structure Discrete Hashing Factorization (LSDHF). Concretely, for different modalities, assisted by the Hadamard matrix, LSDHF aligns all eigenvalues of the similarity matrix to generate a hash dictionary, and then straightly distills the shared hash codes from modalities’ intrinsic structure rather than just preserving the original geometry, so as to strengthen modal connection. In addition, a hyperbolic tangent kernel function is exploited to make the original feature more close to the hash code, thus reducing the mapping loss from the original space to the Hamming space. In the optimization phase, a discrete iterative algorithm is designed for binary optimization without introducing any intermediate variables, or utilizing relaxation strategies. The simulation results on widely used data sets demonstrate the superiority of the proposed strategy competing with state-of-the-art methods, including some supervised cross-modal hashing methods.

迄今为止，在交叉模式哈希社区中，大量保留区分语义的离散哈希模型非常受欢迎。但是，它们中的大多数都是从语义标签中提取共享的哈希码，而忽略了要素的固有结构。因此，本文提出了一种以无监督方式将潜在相似结构分解为跨模式检索而直接提取离散哈希表示框架的方法，我们将其称为潜在结构离散哈希分解（LSDHF）。具体而言，对于不同的模态，在Hadamard矩阵的辅助下，LSDHF对齐相似矩阵的所有特征值以生成哈希字典，然后直接从模态的固有结构中提取共享的哈希码，而不仅仅是保留原始几何图形，从而加强模态联系。另外，利用双曲正切核函数使原始特征更接近哈希码，从而减少了从原始空间到汉明空间的映射损失。在优化阶段，针对二进制优化设计了离散迭代算法，而无需引入任何中间变量或使用松弛策略。在广泛使用的数据集上的仿真结果证明了所提出的策略与最新技术（包括一些监督型跨模式哈希方法）相竞争的优越性。离散迭代算法被设计用于二进制优化，而不引入任何中间变量，也没有利用松弛策略。在广泛使用的数据集上的仿真结果证明了所提出的策略与最新技术（包括一些监督型跨模式哈希方法）相竞争的优越性。离散迭代算法被设计用于二进制优化，而不引入任何中间变量，也没有利用松弛策略。在广泛使用的数据集上的仿真结果证明了所提出的策略与最新技术（包括一些监督型跨模式哈希方法）相竞争的优越性。