Discriminative dual-stream deep hashing for large-scale image retrieval

Highlights：

• Most existing deep supervised hashing methods mainly focus on how to effectively preserve the similarity information of semantic labels while ignoring discrimination information in the labels.

• A novel discriminative dual-stream deep hashing method is proposed to integrate the pairwise similarity loss and the classification loss into a unified framework to take full advantage of label information.

• The proposed method enlarges the margin between the different classes so that can generate discrimination of learned binary codes for better image retrieval performance.

• Extensive experiments show that the proposed method consistently outperforms current state-of- the-art methods on three benchmark datasets for image retrieval task.