Given the benefits of its low storage requirements and high retrieval
efficiency, hashing has recently received increasing attention. In
particular,cross-modal hashing has been widely and successfully used in
multimedia similarity search applications. However, almost all existing methods
employing cross-modal hashing cannot obtain powerful hash codes due to their
ignoring the relative similarity between heterogeneous data that contains
richer semantic information, leading to unsatisfactory retrieval performance.
In this paper, we propose a triplet-based deep hashing (TDH) network for
cross-modal retrieval. First, we utilize the triplet labels, which describes
the relative relationships among three instances as supervision in order to
capture more general semantic correlations between cross-modal instances. We
then establish a loss function from the inter-modal view and the intra-modal
view to boost the discriminative abilities of the hash codes. Finally, graph
regularization is introduced into our proposed TDH method to preserve the
original semantic similarity between hash codes in Hamming space. Experimental
results show that our proposed method outperforms several state-of-the-art
approaches on two popular cross-modal datasets.

本文提出一种基于三元组的深度哈希网络（TDH），用于跨模态检索，并在哈希空间中引入图形正则化，以保留哈希码的原始语义相似性，实验结果表明，我们提出的方法优于几种现有的交叉模态数据集上的最新方法。