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Image matching based on a structured deep coupled metric learning framework

Signal, Image and Video Processing volume 16, pages 1649–1657 (2022)Cite this article

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Abstract

The general similarity metric method is difficult to calculate the similarity of images under different resolutions, occlusion, and deformation. Therefore, this paper proposes a structured deep coupled metric learning framework (SDCML), which provides a solution for image matching under the above special interference. For the input image pairs, the structured region segmentation is performed to derive global and local sub-region images. After that, a stacked sparse auto-encoder network is used to implement deep feature extraction on these derived images. Then, the features with different dimensions of derived image pairs are transformed to coupled space through multi-supervised coupled mapping, and the similarity metric of feature pairs is completed in this space. Finally, all calculated similarities of derived image pairs are fused to obtain the final matching result. The effectiveness of the proposed method is verified by image matching experiments under non-ideal conditions; the optimal recognition rate was 91.98% on Yale-B dataset and 72.80% on Market1501 dataset, respectively.

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Acknowledgements

Funding was provided by Integration Fund of Shandong University of Technology and Zhangdian District (Grant No. 118228) and The National Natural Science Foundation of China (Grant No. 61601266).

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Authors and Affiliations

  1. College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, 255049, China

    Guixia Fu, Guofeng Zou, Mingliang Gao & Zhenzhou Wang

  2. School of Engineering, The University of British Columbia Okanagan Campus, Kelowna, V1V 1V7, Canada

    Zheng Liu

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  1. Guixia Fu
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  2. Guofeng Zou
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  3. Mingliang Gao
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  4. Zhenzhou Wang
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  5. Zheng Liu
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Correspondence to Guofeng Zou.

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Fu, G., Zou, G., Gao, M. et al. Image matching based on a structured deep coupled metric learning framework. SIViP 16, 1649–1657 (2022). https://doi.org/10.1007/s11760-021-02120-z

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  • DOI: https://doi.org/10.1007/s11760-021-02120-z

Keywords

  • Structured region segmentation
  • Deep sparse auto-encoder network
  • Multi-supervised coupled mapping
  • Non-ideal image matching