Visual re-ranking using Nearest Neighbor graph~(NN graph) has been adapted to yield high retrieval accuracy, since it is beneficial to exploring an high-dimensional manifold and applicable without additional fine-tuning. The quality of visual re-ranking using NN graph, however, is limited to that of connectivity, i.e., edges of the NN graph. Some edges can be misconnected with negative images. This is known as a noisy edge problem, resulting in a degradation of the retrieval quality. To address this, we propose a complementary denoising method based on Continuous Conditional Random Field (C-CRF) that uses a statistical distance of our similarity-based distribution. This method employs the concept of cliques to make the process computationally feasible. We demonstrate the complementarity of our method through its application to three visual re-ranking methods, observing quality boosts in landmark retrieval and person re-identification (re-ID).

翻译：利用最近邻图（NN图）进行视觉重排序已被证明能够获得较高的检索精度，因为它有助于探索高维流形结构，且无需额外微调即可应用。然而，基于NN图的视觉重排序质量受限于其连通性，即NN图中的边。部分边可能误连至负样本图像，此现象称为噪声边问题，会导致检索质量下降。为解决该问题，我们提出一种基于连续条件随机场（C-CRF）的互补去噪方法，该方法利用基于相似度的分布统计距离。本方法采用团结构概念以确保计算可行性。通过将所提方法应用于三种视觉重排序方法，我们在标志性场景检索与行人重识别任务中均观察到检索质量的提升，验证了该方法的互补性。