Robust Feature Matching via Graph Neighborhood Motion Consensus

In this paper, we propose an effective method for mismatch removal, termed as graph neighborhood motion consensus, to address the feature matching problem which plays a pivotal role in various computer vision tasks. In our method, we convert each feature correspondence into a motion field sample and model it with the probabilistic graphical model (PGM). To differentiate mismatches from true matches, we firstly design a metric based on neighborhood topology consensus and neighborhood interaction to evaluate the correctness of each match. We also design a variance-based similarity search module to make the information used more reliable for better matching performance. To derive the solution of PGM, we build a model to transform the problem into an integer quadratic programming problem and obtain its closed-form solution with linear time complexity. Extensive experiments on general feature matching, fundamental matrix estimation and image registration tasks demonstrate that our proposed method can achieve superior performance over several state-of-the-art approaches.