High-order graph matching is a problem of establishing the correspondences between two sets of visual features subject to high-order matching constraints. This is an NP-hard combinatorial optimization problem and formulated as a maximization problem of matching score over all permutations of features. Artificial electric field algorithm (AEFA) (Yadav et al., 2019) is a proven optimization algorithm in the family of meta-heuristic and performed well for continuous optimization problems. In this article, we extended the AEFA algorithm for combinatorial high-order graph matching problems and introduced a discrete artificial electric field algorithm (DAEFA). This framework incorporates the redefine position and velocity representation scheme, addition–subtraction operation, velocity and position update rules, and a problem specific initialization by using heuristic information. The efficiency of the proposed algorithm is tested over three well-known datasets: synthetic, CMU house and real-world datasets. The computational results measured the matching score, accuracy of matching and established the correspondences between two graphs. The computational results show the outperformance of the proposed algorithm over the other state-of-art algorithms in terms of good matching score and accuracy both.

高阶图匹配是在受高阶匹配约束的情况下在两组视觉特征之间建立对应关系的问题。这是一个NP困难的组合优化问题，被表述为在所有特征排列上匹配得分的最大化问题。人工电场算法（AEFA）（Yadav et al。，2019）是一种经过证明的元启发式优化算法，对于连续优化问题表现良好。在本文中，我们扩展了用于组合高阶图匹配问题的AEFA算法，并介绍了一种离散人工电场算法（DAEFA）。该框架包含重新定义的位置和速度表示方案，加减运算，速度和位置更新规则，通过使用启发式信息进行特定于问题的初始化。在三个著名的数据集上测试了所提出算法的效率：合成数据集，CMU房屋数据集和真实数据集。计算结果测量了匹配分数，匹配精度并建立了两个图形之间的对应关系。计算结果表明，在良好的匹配分数和准确性方面，该算法优于其他最新算法。