论文标题
跨模式检索的不对称相关量化
Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval
论文作者
论文摘要
由于相似性计算和数据库存储的优越性，用于大规模多种模式数据，跨模式哈希方法在整个异质方式上引起了广泛的关注。但是，仍有一些局限性需要进一步考虑：（1）大多数当前的CMH方法在二进制限制下将实价数据点转换为离散的紧凑型二进制代码，从而限制了原始数据的代表能力，因为信息的大量丢失和产生了次优的HASH代码； （2）很难解决离散的二进制约束学习模型，其中检索性能可以通过放松大量量化误差的二进制约束来大大降低； （3）在对称框架中处理CMH的学习问题，从而导致困难而复杂的优化目标。为了解决上述挑战，在本文中，提出了一种新型的不对称相关量化散列方法（ACQH）方法。 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.此外，可以通过论文中设计的离散迭代优化框架直接获得跨模式的统一哈希码。关于三个基准数据集的全面实验表明了ACQH的有效性和合理性。
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.
