Due to the superiority in similarity computation and database storage for large-scale multiple modalities data, cross-modal hashing methods have attracted extensive attention in similarity retrieval across the heterogeneous modalities. However, there are still some limitations to be further taken into account: (1) most current CMH methods transform real-valued data points into discrete compact binary codes under the binary constraints, limiting the capability of representation for original data on account of abundant loss of information and producing suboptimal hash codes; (2) the discrete binary constraint learning model is hard to solve, where the retrieval performance may greatly reduce by relaxing the binary constraints for large quantization error; (3) handling the learning problem of CMH in a symmetric framework, leading to difficult and complex optimization objective. To address above challenges, in this paper, a novel Asymmetric Correlation Quantization Hashing (ACQH) method is proposed. Specifically, ACQH learns the projection matrixs of heterogeneous modalities data points for transforming query into a low-dimensional real-valued vector in latent semantic space and constructs the stacked compositional quantization embedding in a coarse-to-fine manner for indicating database points by a series of learnt real-valued codeword in the codebook with the help of pointwise label information regression simultaneously. Besides, the unified hash codes across modalities can be directly obtained by the discrete iterative optimization framework devised in the paper. Comprehensive experiments on diverse three benchmark datasets have shown the effectiveness and rationality of ACQH.

中文翻译：

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用于跨模态检索的非对称相关量化散列

由于大规模多模态数据在相似性计算和数据库存储方面的优势，跨模态哈希方法在跨异构模态的相似性检索中引起了广泛关注。然而，仍有一些限制需要进一步考虑：（1）当前大多数 CMH 方法在二进制约束下将实值数据点转换为离散的紧凑二进制代码，由于大量损失限制了原始数据的表示能力信息并产生次优哈希码；(2)离散二元约束学习模型难以求解，对于较大的量化误差，放宽二元约束可能会大大降低检索性能；(3) 在对称框架中处理CMH的学习问题，导致困难和复杂的优化目标。为了解决上述挑战，在本文中，提出了一种新颖的非对称相关量化散列（ACQH）方法。具体来说，ACQH 学习异构模态数据点的投影矩阵，以将查询转换为潜在语义空间中的低维实值向量，并以粗到细的方式构建堆叠组合量化嵌入，以通过一系列指示数据库点同时借助逐点标签信息回归在码本中学习实值码字。此外，跨模态的统一哈希码可以通过本文设计的离散迭代优化框架直接获得。