Quadruplet-based deep hashing for image retrieval

Abstract

Hashing methods have been widely used for large-scale image retrieval. Learning deep hashing networks with a pairwise loss or a triplet ranking loss has become a common framework. The pairwise loss and triplet ranking loss, respectively, focus on preserving the pairwise similarity and the relative similarity ordering. In this paper, we design a quadruplet loss that can fully explore the similarity relation between image pairs to decrease the intraclass variation and increase the interclass variation. Moreover, we propose a deep architecture based on quadruplet loss and optimal adaptive margins for learning hash codes. Extensive experimental results show that our method achieves state-of-the-art performance on several benchmark image retrieval datasets.