In light of the ability to enable efficient storage and fast query for big data, hashing techniques for cross-modal search have aroused extensive attention. Despite the great success achieved, unsupervised cross-modal hashing still suffers from lacking reliable similarity supervision and struggles with handling the heterogeneity issue between different modalities. To cope with these, in this paper, we devise a new deep hashing model, termed as High-order Nonlocal Hashing (HNH) to facilitate cross-modal retrieval with the following advantages. First, different from existing methods that mainly leverage low-level local-view similarity as the guidance for hashing learning, we propose a high-order affinity measure that considers the multi-modal neighbourhood structures from a nonlocal perspective, thereby comprehensively capturing the similarity relationships between data items. Second, a common representation is introduced to correlate different modalities. By enforcing the modal-specific descriptors and the common representation to be aligned with each other, the proposed HNH significantly bridges the modality gap and maintains the intra-consistency. Third, an effective affinity preserving objective function is delicately designed to generate high-quality binary codes. Extensive experiments evidence the superiority of the proposed HNH in unsupervised cross-modal retrieval tasks over the state-of-the-art baselines.

鉴于能够高效存储和快速查询大数据的能力，用于跨模式搜索的哈希技术引起了广泛的关注。尽管取得了巨大的成功，但无监督的跨模式散列仍然缺乏可靠的相似性监督，并且在处理不同模式之间的异质性问题上也遇到了困难。为了解决这些问题，在本文中，我们设计了一种新的深度哈希模型，称为高阶非局部哈希（HNH），以促进具有以下优点的跨模式检索。首先，与主要利用低级本地视图相似性作为哈希学习指导的现有方法不同，我们提出了一种从非本地角度考虑多模式邻域结构的高阶亲和力度量，从而全面捕获数据项之间的相似关系。其次，引入了一种通用表示法来关联不同的模态。通过强制特定于模态的描述符和公共表示彼此对齐，提出的HNH显着弥合了模态鸿沟，并保持了内部一致性。第三，精心设计了有效的保持亲和力的目标函数，以生成高质量的二进制代码。大量的实验证明了所提出的HNH在无人监督的交叉模式检索任务中优于现有基准的优越性。拟议的HNH极大地弥合了模态鸿沟，并保持了内部一致性。第三，精心设计了有效的保持亲和力的目标函数，以生成高质量的二进制代码。大量的实验证明了所提出的HNH在无人监督的交叉模式检索任务中优于现有基准的优越性。拟议的HNH极大地弥合了模态鸿沟，并保持了内部一致性。第三，精心设计了有效的保持亲和力的目标函数，以生成高质量的二进制代码。大量的实验证明了所提出的HNH在无人监督的交叉模式检索任务中优于现有基准的优越性。