Deep hashing has been widely applied in large scale image retrieval due to its high computation efficiency and retrieval performance. Recently, training deep hashing networks with a triplet ranking loss become a common framework. However, most of the triplet ranking loss based deep hashing methods cannot obtain satisfactory retrieval performance due to their ignoring the relative similarities among the objects. In this paper, we propose a method to learn the discriminative object features and utilize these features to compute the adaptive margins of the proposed loss for learning powerful hash codes. Experimental results show that our learned hash codes can yield state-of-the-art retrieval performance on three challenging datasets

深度哈希技术由于其高计算效率和检索性能而被广泛应用于大规模图像检索中。最近，训练具有三元组排名损失的深度哈希网络已成为常见的框架。但是，大多数基于三元组排序损失的深度哈希方法由于忽略了对象之间的相对相似性，因此无法获得令人满意的检索性能。在本文中，我们提出了一种学习判别对象特征并利用这些特征来计算所提出的损失的自适应余量的方法，以学习强大的哈希码。实验结果表明，我们学到的哈希码可以在三个具有挑战性的数据集上产生最先进的检索性能