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Graph matching based on feature and spatial location information

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Abstract

Graph matching (GM) is a crucial task in the fields of computer vision. It aims at finding node-to-node correspondences between two graphs. In this paper, we propose a new GM method. We combine feature and spatial location information to construct a mixture dissimilarity matrix and compensate for the deficiency that previous methods consider only feature information. The element of this matrix is used to measure the difference in a pair of nodes. First, feature dissimilarity is measured using a new shape context descriptor. Second, spatial location dissimilarity is obtained by capturing local and global information of nodes. In accordance with the mixture dissimilarity matrix, the initial correspondences are established using the Hungarian method. Lastly, our method solves two objective functions and obtains the matching results. The experimental results for three commonly used datasets verify the efficiency and advantages of our method relative to the current methods.

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Acknowledgements

This research was supported by Natural Science Foundation of Shandong province (Nos. ZR2019MF013, ZR2019BF026), Project of Jinan Scientific Research Leader’s Laboratory (No. 2018GXRC023).

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Authors and Affiliations

  1. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China

    Chuanju Liu, Dongmei Niu & Xiuyang Zhao

  2. School of Information Science and Engineering, University of Jinan, Jinan, China

    Chuanju Liu, Dongmei Niu & Xiuyang Zhao

  3. Shandong Labor Vocational and Technical College, Jinan, China

    Chuanju Liu

  4. Qingdao University of Science and Technology, Qingdao, China

    Xinghai Yang

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  1. Chuanju Liu
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  2. Dongmei Niu
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Liu, C., Niu, D., Yang, X. et al. Graph matching based on feature and spatial location information. Vis Comput 39, 711–722 (2023). https://doi.org/10.1007/s00371-021-02369-y

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