Semantic hashing enables computation and memory-efficient image retrieval through learning similarity-preserving binary representations. Most existing hashing methods mainly focus on preserving the piecewise class information or pairwise correlations of samples into the learned binary codes while failing to capture the mutual triplet-level ordinal structure in similarity preservation. In this article, we propose a novel Probability Ordinal-preserving Semantic Hashing (POSH) framework, which for the first time defines the ordinal-preserving hashing concept under a non-parametric Bayesian theory. Specifically, we derive the whole learning framework of the ordinal similarity-preserving hashing based on the maximum posteriori estimation, where the probabilistic ordinal similarity preservation, probabilistic quantization function, and probabilistic semantic-preserving function are jointly considered into one unified learning framework. In particular, the proposed triplet-ordering correlation preservation scheme can effectively improve the interpretation of the learned hash codes under an economical anchor-induced asymmetric graph learning model. Moreover, the sparsity-guided selective quantization function is designed to minimize the loss of space transformation, and the regressive semantic function is explored to promote the flexibility of the formulated semantics in hash code learning. The final joint learning objective is formulated to concurrently preserve the ordinal locality of original data and explore potentials of semantics for producing discriminative hash codes. Importantly, an efficient alternating optimization algorithm with the strictly proof convergence guarantee is developed to solve the resulting objective problem. Extensive experiments on several large-scale datasets validate the superiority of the proposed method against state-of-the-art hashing-based retrieval methods.

中文翻译：

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用于大规模图像检索的概率序保持语义散列

语义哈希通过学习保持相似性的二进制表示来实现计算和内存高效的图像检索。大多数现有的散列方法主要侧重于将样本的分段类信息或成对相关性保存到学习的二进制代码中，而在相似性保存中未能捕获相互的三元级序数结构。在本文中，我们提出了一种新颖的保留概率序数语义哈希（POSH）框架，该框架首次定义了非参数贝叶斯理论下的序数保留哈希概念。具体来说，我们基于最大后验估计，其中概率序数相似性保持、概率量化函数和概率语义保持函数被共同考虑到一个统一的学习框架中。特别是，所提出的三元排序相关性保留方案可以有效地改善在经济锚诱导非对称图学习模型下学习的哈希码的解释。此外，设计了稀疏引导的选择性量化函数以最小化空间变换的损失，探索回归语义函数以提高哈希码学习中公式化语义的灵活性。最终的联合学习目标被制定为同时保留原始数据的序数局部性并探索语义的潜力以产生有区别的哈希码。重要的是，开发了一种具有严格证明收敛保证的有效交替优化算法来解决由此产生的目标问题。在几个大规模数据集上的广泛实验验证了所提出的方法相对于最先进的基于散列的检索方法的优越性。