In this paper, we present a new cluster-wise unsupervised hashing (CUH) approach to learn compact binary codes for cross-modal similarity retrieval. We develop a discrete optimization method to jointly learn binary codes and the corresponding hash functions for each modality which can improve the performance, unlike existing cross-modal hashing methods that often drop the binary constraints to obtain the binary codes. Moreover, considering the semantic consistency between observed modalities, our CUH generates one unified hash code for all observed modalities of any instance. Specifically, we construct a co-training framework for learning to hash, in which we simultaneously realize the multi-view clustering and the learning of hash. Firstly, our CUH utilize the re-weighted discriminatively embedded K-means for multi-view clustering to learn the corresponding dimension reduced data and the cluster centroid points in the low-dimensional common subspaces, which are used as the approximation to the corresponding hash codes of original data and the cluster-wise code-prototypes respectively. Secondly, in the process for learning of hash, these cluster-wise code-prototypes can guide the learning of the codes to further improve the performance of the binary codes. The reasonableness and effectiveness of CUH is well demonstrated by comprehensive experiments on diverse benchmark datasets.

中文翻译：

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用于跨模态相似性搜索的集群无监督散列

在本文中，我们提出了一种新的集群方式无监督哈希（CUH）方法来学习用于跨模态相似性检索的紧凑二进制代码。我们开发了一种离散优化方法来联合学习二进制代码和每个模态的相应散列函数，这可以提高性能，这与现有的跨模态散列方法不同，这些方法通常会放弃二进制约束以获得二进制代码。此外，考虑到观察模态之间的语义一致性，我们的 CUH 为任何实例的所有观察模态生成一个统一的哈希码。具体来说，我们构建了一个学习哈希的协同训练框架，其中我们同时实现了多视图聚类和哈希的学习。首先，我们的 CUH 利用重新加权的判别嵌入 K 均值进行多视图聚类来学习相应的降维数据和低维公共子空间中的聚类质心点，用作原始相应哈希码的近似值数据和集群方式的代码原型。其次，在学习hash的过程中，这些cluster-wise的code-prototype可以指导code的学习，进一步提高二进制code的性能。CUH 的合理性和有效性通过对不同基准数据集的综合实验得到了很好的证明。它们分别用作原始数据和集群代码原型的相应哈希码的近似值。其次，在学习hash的过程中，这些cluster-wise code-prototype可以指导代码的学习，进一步提高二进制代码的性能。CUH 的合理性和有效性通过对不同基准数据集的综合实验得到了很好的证明。它们分别用作原始数据和集群代码原型的相应哈希码的近似值。其次，在学习hash的过程中，这些cluster-wise的code-prototype可以指导code的学习，进一步提高二进制code的性能。CUH 的合理性和有效性通过对不同基准数据集的综合实验得到了很好的证明。