Graph matching based point correspondence with alternating direction method of multipliers

Graph matching is a fundamental problem in image processing and computer vision tasks, which can be used to solve the feature correspondence problem. Most graph matching tasks have been proved to be NP-Complete problems which are generally solved by approximate algorithms based on the continuous relaxation scheme. Targeting at these problems, this paper proposes a novel graph matching algorithm based on the alternating direction method of multipliers (ADMM) which typically decomposes a difficult problem into relatively simpler subproblems. Specifically, the matching constraints are decomposed into four continuous constraints and the assignment vector is decomposed into different vectors according to the constraints. Then the transformed objective function is iteratively optimized under the framework of ADMM. The proposed method achieves the state of the art performance, while maintaining a comparable computational complexity. And a discrete solution can be directly obtained without the projection pro-cess. Experimental results on both synthetic points and real images demonstrate the effectiveness of the proposed method by comparing it with the state-of-the-art methods. (c) 2021 Published by Elsevier B.V.