A Novel and Effective Cooperative RANSAC Image Matching Method Using Geometry Histogram-Based Constructed Reduced Correspondence Set

The success of many computer vision and pattern recognition applications depends on matching local features on two or more images. Because the initial correspondence set—i.e., the set of the initial feature pairs—is often contaminated by mismatches, removing mismatches is a necessary task prior to image matching. In this paper, we first propose a fast geometry histogram-based (GH-based) mismatch removal strategy to construct a reduced correspondence set Creduced,GH from the initial correspondence set Cini. Next, we propose an effective cooperative random sample consensus (COOSAC) method for remote sensing image matching. COOSAC consists of a RANSAC, called RANSACini working on Cini, and a tiny RANSAC, called RANSACtiny,GH working on a randomly selected subset of Creduced,GH. In RANSACtiny,GH, an iterative area constraint-based sampling strategy is proposed to estimate the model solution of Ctiny,GH until the specified confidence level is reached, and then RANSACini utilizes the estimated model solution of Ctiny,GH to calculate the inlier rate of Cini. COOSAC repeats the above cooperation between RANSACtiny,GH and RANSACini until the specified confidence level is reached, reporting the resultant model solution of Cini. For convenience, our image matching method is called the GH-COOSAC method. Based on several testing datasets, thorough experimental results demonstrate that the proposed GH-COOSAC method achieves lower computational cost and higher matching accuracy benefits when compared with the state-of-the-art image matching methods