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Image retrieval based on dimensionality reduction of second-order information

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Abstract

Identifying similar images within databases is a significant challenge in the field of image retrieval. This challenge is further compounded by escalating demands for heightened precision and speed, propelled by advancements in information technology. This study introduces a distinctive network design to address common problems associated with high-probability image retrieval, such as sluggish retrieval speeds and inadequate discrimination. The network architecture primarily employs CNNs for feature extraction. To maximize the advantages of deep learning in feature extraction, the design includes a second-order attention module and a second-order similarity loss. The incorporation of second-order information enhances the correlation among local spatial locations in the image, adjusting each local contribution to the global context. This facilitates a more comprehensive understanding of the image's structure and content, thereby improving feature extraction. Furthermore, dimension reduction techniques are applied to effectively capture the profound information within images. This aids in the elimination of superfluous features while preserving essential information necessary for the retrieval task. This method improves the model's retrieval accuracy by concentrating on the image's deep features, allowing it to disregard unnecessary information. Experimental results demonstrate that our image retrieval technique not only improves accuracy but also significantly accelerates retrieval speed across various datasets, exceeding previous benchmarks in the field.

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Data availability

The data supporting the findings of this study are publicly available datasets. http://doi.org/https://doi.org/10.1109/CVPR.2018.00598.

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Acknowledgements

At the end of the article, I would like to thank Teacher Zhang and Teacher Liu for their guidance and help.

Funding

This study is not applicable.

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Authors and Affiliations

  1. Shanghai University of Electric Power, No. 1851 Huchenghuan Road, Pudong New Area, Shanghai, 201306, China

    Fuqiang Wu & Dandan Liu

  2. Shanghai Normal University, No. 100, Haixinhua, Fengxian District, Shanghai, 201418, China

    Kang An & Hui Zhang

Authors
  1. Fuqiang Wu
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  2. Dandan Liu
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  3. Kang An
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  4. Hui Zhang
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Contributions

Fuqiang Wu: Conceptualization, Data curation, Writing-Original draft preparation. Writing- Reviewing and Editing. Dandan Liu and Kang An: Investigation, Supervision. Hui Zhang: Methodology, Supervision, Validation.

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Correspondence to Hui Zhang.

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Wu, F., Liu, D., An, K. et al. Image retrieval based on dimensionality reduction of second-order information. SIViP 18, 2723–2731 (2024). https://doi.org/10.1007/s11760-023-02943-y

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