Discriminative deep metric learning for asymmetric discrete hashing

Abstract Thanks to its storage and computation efficiency, hashing as a kind of nearest neighbor search method can facilitate massive data processing in recent vision and learning studies. Particularly, deep supervised hashing methods have significantly improved the retrieval performance compared with non-deep supervised hashing methods. However, most existing deep supervised hashing methods approximate the similarity between two images with the hamming distance between the outputs of the same hash function, i.e., the symmetric strategy. Consequently, it is typically time-consuming to train these symmetric hashing methods, and these methods can hardly take full advantage of the supervised information in the large-scale database. In this paper, we propose a novel discriminative deep metric learning approach for asymmetric discrete hashing (ADMH) approach for supervised hashing learning. ADMH integrates an asymmetric strategy with a deep metric learning method to learn the hash function for the query images and the discrete hash codes for database images directly. More specifically, we train a deep neural network to extract the features of the query images. Subsequently, we introduce a metric learning scheme for learning the discrete discriminative hash codes of the database images directly. Finally, the feature learning procedure for generating the hash codes of the query images and the discrete coding procedure for generating the hash codes of the database images are integrated into an end-to-end learning framework. Extensive experiments on various benchmark datasets show that the proposed asymmetric deep hashing method outperforms the existing hashing methods.