The hashing method is widely used for large-scale image retrieval due to its low time and space complexity. However, the existing deep hashing methods are mainly designed for labeled datasets. Without supervised information, retrieval performance on unlabeled datasets is not guaranteed. In this letter, we propose a novel deep hashing approach for unsupervised image retrieval applications. The contributions are two-fold. First, the pseudolabels are generated using their global features aggregated from the pretrained network and employed as self-supervised information to optimize the objective function of training. Second, adaptive feature learning is used in this deep hashing framework to perform simultaneous hash function learning and global features learning in an unsupervised manner. The experimental results validated the effectiveness of the proposed method, obtaining state-of-the-art performances on several public datasets such as CIFAR-10, Holidays, and Oxford5k.