The hashing technique has been extensively used in large-scale image retrieval applications due to its low storage and fast computing speed. Most existing deep hashing approaches cannot fully consider the global semantic similarity and category-level semantic information, which result in the insufficient utilization of the global semantic similarity for hash codes learning and the semantic information loss of hash codes. To tackle these issues, we propose a novel deep hashing approach with triplet labels, namely, deep category-level and regularized hashing (DCRH), to leverage the global semantic similarity of deep feature and category-level semantic information to enhance the semantic similarity of hash codes. There are four contributions in this article. First, we design a novel global semantic similarity constraint about the deep feature to make the anchor deep feature more similar to the positive deep feature than to the negative deep feature. Second, we leverage label information to enhance category-level semantics of hash codes for hash codes learning. Third, we develop a new triplet construction module to select good image triplets for effective hash functions learning. Finally, we propose a new triplet regularized loss (Reg-L) term, which can force binary-like codes to approximate binary codes and eventually minimize the information loss between binary-like codes and binary codes. Extensive experimental results in three image retrieval benchmark datasets show that the proposed DCRH approach achieves superior performance over other state-of-the-art hashing approaches.

中文翻译：

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具有全局语义相似性学习的深度类别级和正则化散列

散列技术由于其低存储和快速计算速度而被广泛应用于大规模图像检索应用中。大多数现有的深度哈希方法不能充分考虑全局语义相似度和类别级语义信息，导致全局语义相似度在哈希码学习中的利用不足，导致哈希码的语义信息丢失。为了解决这些问题，我们提出了一种新的带有三元组标签的深度哈希方法，即深度类别级和正则化哈希（DCRH），以利用深度特征和类别级语义信息的全局语义相似性来增强语义相似性。哈希码。本文有四个贡献。第一的，我们设计了一个关于深度特征的新的全局语义相似性约束，使锚深度特征更类似于正深度特征而不是负深度特征。其次，我们利用标签信息来增强用于哈希码学习的哈希码的类别级语义。第三，我们开发了一个新的三元组构建模块来选择好的图像三元组进行有效的哈希函数学习。最后，我们提出了一个新的三元组正则化损失 (Reg-L) 项，它可以强制类二进制代码逼近二进制代码，并最终最大限度地减少类二进制代码和二进制代码之间的信息丢失。在三个图像检索基准数据集中的大量实验结果表明，所提出的 DCRH 方法比其他最先进的散列方法具有更好的性能。