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Asymmetric hashing based on generative adversarial network

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Abstract

In the era of big data, social media, large-scale video, image, and text data is produced every day. The approximate nearest neighbor (ANN) search has drawn significant attention for content-based image retrieval applications to ensure retrieval quality and computational efficiency. Hashing has become a cutting-edge technology for image retrieval and big data applications due to its low-storage and high-computational efficiency. Hashing algorithms are useful for mapping images into short binary codes and generating a similar binary code for similar data points from the database. Many supervised/unsupervised hashing methods have been deployed for retrieving the query points from the database images, and many recently developed methods can achieve a higher accuracy regarding image retrieval performance. However, the current state-of-the-art algorithms can only improve binary code hashing, and the retrieval performance of binary representation is not good. To overcome this issue, we propose an asymmetric learning method that generates the hash codes. This work proposes a novel asymmetric learning-based generative adversarial network (AGAN) for image retrieval, which integrates the feature learning with hashing to an end-to-end learning framework. Moreover, to equip with the binary representation of image retrieval; we propose three loss functions, i.e., encoder loss, generator loss, and discriminator loss, which significantly improve retrieval performance. The extensive experiments show that our proposed method outperformed several state-of-the-art methods.

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Acknowledgements

We would like to express our gratitude to anonymous reviewers for their careful examination of our manuscript and providing us constructive feedback to help us improve the quality of our proposed work.

Funding

This research was supported by the National Nature Science Foundation of China (No. 62102163) and the Natural Science Foundation of Shandong Province (Nos. ZR2019MF013, ZR2019BF026, ZR2020KF015).

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Author notes

  1. Muhammad Umair Hassan

    Present address: Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), Ã…lesund, Norway

Authors and Affiliations

  1. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, People’s Republic of China

    Muhammad Umair Hassan, Dongmei Niu & Xiuyang Zhao

  2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan Province, People’s Republic of China

    Mingxuan Zhang

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  1. Muhammad Umair Hassan
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Correspondence to Xiuyang Zhao.

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Hassan, M.U., Niu, D., Zhang, M. et al. Asymmetric hashing based on generative adversarial network. Multimed Tools Appl 82, 389–405 (2023). https://doi.org/10.1007/s11042-022-13141-2

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