Spectral-spatial Outlier Filter for Image Matching

Feature-based image matching is often contaminated by some mismatches due to the limited representation of descriptors. Existing two-stage mismatch removal filters usually select some seed points first and then remove outliers according to the consistency in neighborhoods. However, the filter's performance is directly influenced by the selection of effective seed points. In this paper, we design an elegant Spectral-spatial Outlier Filter (SOF) to harvest high-accuracy image matching. Specifically, we first calculate eigenvectors of Laplacian matrix from the joint image graph as feature descriptors in the spectral domain to select more reasonable seed points, and then these points are fed into the local affine verification in the spatial domain in the second stage to effectively remove outliers. Experimental results on challenging datasets demonstrate that the proposed filter further improves the precision of image matching, and steadily outperforms other state-of-the-art methods.