MatchDR: Image Correspondence by Leveraging Distance Ratio Constraint

Abstract

Image correspondence is to establish the connections between coherent images, which can be quite challenging due to the visual and geometric deformations. This paper proposes a robust image correspondence technique from the perspective of spatial regularity. Specifically, the visual deformation is addressed by introducing the spatial information by enforcing the distance ratio constrain. At the same time, the geometric deformation is tolerated by adopting a smoothness term. Subsequently, image correspondence is formulated as permutation problem, for which, we propose a Gradient Guided Simulated Annealing method for robust optimization. Furthermore, our method is much more memory efficient, where the storage complexity is reduced from O(n4) to O(n2). The experiments on several datasets indicate that our proposed formulation and optimization significantly improve the baselines for both visually-similar and semantically-similar images, where both visual and geometric deformations are present.