  Recently, similarity-preserving hashing methods have been extensively studied
for large-scale image retrieval. Compared with unsupervised hashing, supervised
hashing methods for labeled data have usually better performance by utilizing
semantic label information. Intuitively, for unlabeled data, it will improve
the performance of unsupervised hashing methods if we can first mine some
supervised semantic 'label information' from unlabeled data and then
incorporate the 'label information' into the training process. Thus, in this
paper, we propose a novel Object Detection based Deep Unsupervised Hashing
method (ODDUH). Specifically, a pre-trained object detection model is utilized
to mining supervised 'label information', which is used to guide the learning
process to generate high-quality hash codes.Extensive experiments on two public
datasets demonstrate that the proposed method outperforms the state-of-the-art
unsupervised hashing methods in the image retrieval task.
