Unsupervised Deep Hashing via Adaptive Clustering

Similarity-preserved hashing has become a popular technique for large-scale image retrieval because of its low storage cost and high search efficiency. Unsupervised hashing has high practical value because it learns hash functions without any annotated label. Previous unsupervised hashing methods usually obtain the semantic similarities between data points by taking use of deep features extracted from pre-trained CNN networks. The semantic structure learned from fixed embeddings are often not the optimal, leading to sub-optimal retrieval performance. To tackle the problem, in this paper, we propose a Deep Clustering based Unsupervised Hashing architecture, called DCUH. The proposed model can simultaneously learn the intrinsic semantic relationships and hash codes. Specifically, DCUH first clusters the deep features to generate the pseudo classification labels. Then, DCUH is trained by both the classification loss and the discriminative loss. Concretely, the pseudo class label is used as the supervision for classification. The learned hash code should be invariant under different data augmentations with the local semantic structure preserved. Finally, DCUH is designed to update the cluster assignments and train the deep hashing network iteratively. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art unsupervised hashing methods.