Graph hashing network for image retrieval

Abstract

Deep supervised hashing is more popular among researchers due to its satisfactory computational efficiency and retrieval performance. Most existing models learn hash codes for data by constructing inter-sample pair-wise or triplet losses, allowing for consideration of the topological information from the label space. However, the topological relationships among samples in the feature space are not fully explored, which may result in less discriminative hash codes. To address this issue, we propose a novel graph hashing network (GHash) for image retrieval. Our GHash explores positional relationships among samples under a large receptive field through alternating updates of graph nodes and edges, generating high-quality image descriptors based on optimized positional relationships and neighborhood information. Subsequently, graph-level descriptors are mapped into highly discriminative hash codes. Additionally, we introduce an extra classification loss to enhance the accuracy of the topological relationships among samples in the graph by supervising the learning of edge features. Finally, we conduct extensive comparison and ablation experiments on three benchmark datasets, with results demonstrating that our method achieves superior retrieval performance compared to state-of-the-art deep hashing methods.