Gaussian field consensus: A robust nonparametric matching method for outlier rejection

In this paper, we propose a robust method, called Gaussian Field Consensus (GFC), for outlier rejection from given putative point set matching correspondences. Finding correct correspondences (inliers) is a key component in many computer vision and pattern recognition tasks, and the goal of outlier (mismatch) rejection is to fit the transformation function that maps one feature point set to another. Our GFC starts by inputting a putative correspondence set which is contaminated by many outliers, and the main task of our GFC is to identify the underlying true correspondences from outliers. Then we formulate this challenging problem by Gaussian Field nonparametric matching model which bases on the exponential distance loss and kernel method in a reproducing kernel Hilbert space. Next, We introduce a local linear constraint based on the regularization theory to preserve the topological structure of the feature points. Moreover, the sparse approximation is used to reduce the search space, in this way, we can handle a large number of points easily. Finally, we test the GFC method on several real image datasets in the presence of outliers, where the experimental results show that our proposed method outperforms current state-of-the-art methods in most test scenarios.