Abstract
Abstract High-order graph matching utilizes the high-order relations to establish the correspondences between two sets of features. It has been formulated as an optimization problem, and this problem is an NP-hard combinatorial optimization problem. This paper presents a high-order graph matching method based on ant colony optimization. A problem-specific pheromone initialization method is introduced, meanwhile local and global pheromone updating rules are redefined. The proposed algorithm is evaluated on synthetic datasets, CMU House dataset, and real-world datasets. Experimental results demonstrate that our algorithm achieves the promising performance compared with three state-of-the-art methods.
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