用连续CRF消除图像检索中的错误连接，提升排序精度。
Enhancing Visual Re-ranking through Denoising Nearest Neighbor Graph via Continuous CRF
- 基于连续CRF构建邻域图，通过统计距离过滤噪声边。
- 在三种检索方法上均显著提升准确率，无须额外微调。
- 离线计算高效，适合大规模图像检索场景。
基于最近邻（NN）图的视觉重排序已成为提升检索准确性的有力方法，可在不需额外微调的情况下有效探索高维流形。然而，其性能受限于边连接质量，因不相似图像间常出现错误连接，即噪声边问题，阻碍了现有技术的性能发挥。为此，我们提出一种基于连续条件随机场（C-CRF）的互补去噪方法，利用基于相似性分布的统计距离。作为增强NN图检索的预处理步骤，该方法为每个锚点图像构建全连接团块，并采用新型统计距离度量，在重排序前稳健缓解噪声边，同时通过离线计算实现高效处理。大量实验表明，该方法持续提升三种不同的NN图重排序方法，显著提高检索准确率。
原文摘要 · Abstract (English)
Nearest neighbor (NN) graph based visual re-ranking has emerged as a powerful approach for improving retrieval accuracy, offering the advantages of effectively exploring high-dimensional manifolds without requiring additional fine-tuning. However, the effectiveness of NN graph-based re-ranking is fundamentally constrained by the quality of its edge connectivity, as incorrect connections between dissimilar (negative) images frequently occur. This is known as a noisy edge problem, which hinders the re-ranking performance of existing techniques and limits their potential. To remedy this issue, we propose a complementary denoising method based on Continuous Conditional Random Fields (C-CRF) that leverages statistical distances derived from similarity-based distributions. As a pre-processing step for enhancing NN graph-based retrieval, our approach constructs fully connected cliques around each anchor image and employs a novel statistical distance metric to robustly alleviate noisy edges before re-ranking while achieving efficient processing through offline computation. Extensive experimental results demonstrate that our method consistently improves three different NN graph-based re-ranking approaches, yielding significant gains in retrieval accuracy.
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