Abstract
To solve the current problem of low retrieval accuracy in deep hashing image retrieval, an improved generative adversarial network (GAN)-based image hashing retrieval method using supervised contrast learning (SconGAN) is proposed. The method augments training samples by introducing dense residual blocks and content fidelity items into the generative network to synthesize more diverse images with higher quality; moreover, it enhances the feature discrimination ability of the discriminative network by introducing pyramidal convolution and supervised contrast learning. Meanwhile, it improves the hashing code generation quality by introducing new pairwise similarity loss, semantic retention loss and quantization loss into the hashing network. In addition, discriminative networks and hashing networks share the core network structure to reduce resource consumption and improve training efficiency. Comprehensive experiments on the CIFAR-10 and NUS-WIDE benchmark datasets show that the proposed method greatly outperforms the comparison methods and obtains the best mean average precision (MAP).
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Acknowledgements
The authors would like to thank the editors and reviewers for their outstanding comments and suggestions, which provide us with momentum and guidance to make deeper research into our subject matter and further improve our paper.This work was supported by the National Natural Science Foundation of China under Grants 41871226 and 41571401, the Science and Technology Research Project of Henan Province under Grants 212102210492, and the Science and Technology Research Project of Nanyang City under Grants KJGG102.
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Lei, L., Guo, D., Shen, Z. et al. Image hashing retrieval based on generative adversarial networks. Appl Intell 53, 9056–9067 (2023). https://doi.org/10.1007/s10489-022-03970-x
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DOI: https://doi.org/10.1007/s10489-022-03970-x