Unsupervised deep hashing with node representation for image retrieval

Highlights：

• An unsupervised deep hashing framework that consists of node representation learning stage and hash function learning stage is proposed.

• In the first stage, we utilize graph convolution network to integrate the relationships between samples into node representations.

• In the second stage, we use above node representations to fast achieve an end-to-end hash model to generate semantic hash codes.

• Extensive experiments show NRDH can achieve higher performance and outperform the state-of-the-art unsupervised deep hashing methods.