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Anchor-based Domain Adaptive Hashing for unsupervised image retrieval

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Abstract

Traditional image retrieval methods suffer from a significant performance degradation when the model is trained on the target dataset and run on another dataset. To address this issue, Domain Adaptive Retrieval (DAR) has emerged as a promising solution, specifically designed to overcome domain shifts in retrieval tasks. However, existing unsupervised DAR methods still face two primary limitations: (1) they under-explore the intrinsic structure among domains, resulting in limited generalization capabilities; and (2) the models are often too complex to be applied to large-scale datasets. To tackle these limitations, we propose a novel unsupervised DAR method named Anchor-based Domain Adaptive Hashing (ADAH). ADAH aims to exploit the commonalities among domains with the assumption that a consensus latent space exists for the source and target domains. To achieve this, an anchor-based similarity reconstruction scheme is proposed, which learns a set of domain-shared anchors and domain-specific anchor graphs, and then reconstructs the similarity matrix with these anchor graphs, thereby effectively exploiting inter- and intra-domain similarity structures. Subsequently, by treating the anchor graphs as feature embeddings, we solve the Distance-Distance Difference Minimization (DDDM) problem between them and their corresponding hash codes. This preserves the similarity structure of the similarity matrix in the hash code. Finally, a two-stage strategy is employed to derive the hash function, ensuring its effectiveness and scalability. Experimental results on four datasets demonstrate the effectiveness of the proposed method.

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Data availability

The MNIST-USPS, VLCS, and Cross-dataset Testbed datasets used during the current study can be accessed via https://github.com/jindongwang/transferlearning/tree/master/data. The Office-Home dataset used during the current study is publicly available at https://www.hemanthdv.org/officeHomeDataset.html.

Notes

  1. https://github.com/jindongwang/transferlearning/tree/m-aster/data.

  2. http://hemanthdv.org/OfficeHome-Dataset/.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 62176065 and 62202107, in part by the Guangdong Provincial Natural Science Foundation under Grant No. 2022A1515011277, and in part by the School level scientific research project of Guangdong Polytechnic Normal University under Grant No. 2021SDKYA013.

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Authors and Affiliations

  1. School of Computer, Guangdong University of Technology, Guangzhou, 510006, China

    Yonghao Chen, Yuanyuan Liu, Xi Hu & Peipei Kang

  2. School of Automation, Guangdong University of Technology and Guangdong Provincial Key Laboratory of Minimally Invasive Surgical Instruments and Manufacturing Technology (GDUT), Guangzhou, 510006, China

    Xiaozhao Fang

  3. School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China

    Na Han

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  1. Yonghao Chen
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Correspondence to Na Han.

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Chen, Y., Fang, X., Liu, Y. et al. Anchor-based Domain Adaptive Hashing for unsupervised image retrieval. Int. J. Mach. Learn. & Cyber. 15, 6011–6026 (2024). https://doi.org/10.1007/s13042-024-02298-x

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