This article studies the correspondence problem for semantically similar images, which is challenging due to the joint visual and geometric deformations. We introduce the Flip-aware Distance Ratio method (FDR) to solve this problem from the perspective of geometric structure analysis. First, a distance ratio constraint is introduced to enforce the geometric consistencies between images with large visual variations, whereas local geometric jitters are tolerated via a smoothness term. For challenging cases with symmetric structures, our proposed method exploits Curl to suppress the mismatches. Subsequently, image correspondence is formulated as a permutation problem, for which we propose a Gradient Guided Simulated Annealing (GGSA) algorithm to perform a robust discrete optimization. Experiments on simulated and real-world datasets, where both visual and geometric deformations are present, indicate that our method significantly improves the baselines for both visually and semantically similar images.

中文翻译：

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几何结构分析的语义对应

本文研究语义相似图像的对应问题，由于联合视觉和几何变形，这具有挑战性。我们从几何结构分析的角度引入了翻转感知距离比方法（FDR）来解决这个问题。首先，引入距离比约束以强制具有较大视觉变化的图像之间的几何一致性，而通过平滑项来容忍局部几何抖动。对于具有对称结构的具有挑战性的情况，我们提出的方法利用 Curl 来抑制不匹配。随后，图像对应被表述为一个置换问题，为此我们提出了一种梯度引导模拟退火 (GGSA) 算法来执行稳健的离散优化。在模拟和真实世界数据集上进行实验，