Image feature matching guided by local density difference

Abstract:

To address the low generalization and poor robustness in feature matching methods that rely on pre-defined parameterized models, based on the observation that the spatial distribution of correct match and mismatch has significant differences, a feature matching algorithm guided by local density difference (RFM-LoDD) is proposed. Firstly, the putative feature matches are converted into spatial sample points that can characterize the nature of the feature matches, and the probabilistic distance is introduced to calculate the local density of the sample points. Secondly, the optimal parameter settings of the algorithm are tested on 40 randomly selected image pairs involving different transformation models,determining the globally optimal density threshold and other parameters. Finally, the local density of the sample points is compared with the density threshold. When the local density of a sample point is greater than the density threshold, the putative feature match represented by the sample point is considered to be a correct match, otherwise, it is a mismatch. Experiments conducted on representative image pairs and public datasets demonstrate that the RFM-LoDD algorithm maintains good robustness in various matching scenarios. Notably, it achieves leading F-scores on the Retina dataset and AdelaideRMF dataset with low inlier rates compared to advanced algorithms. Additionally, the RFM-LoDD algorithm has quasi-linear time complexity, with an average run time of about 40 ms on the four public datasets, significantly reducing time cost by two orders of magnitude compared to the classical random sample consensus (RANSAC) algorithm.