Enhancing Visual Re-ranking through Denoising Nearest Neighbor Graph via Continuous CRF

• URL: http://arxiv.org/abs/2412.13875v2
• Date: Tue, 08 Jul 2025 11:35:03 GMT
• Title: Enhancing Visual Re-ranking through Denoising Nearest Neighbor Graph via Continuous CRF
• Authors: Jaeyoon Kim, Yoonki Cho, Taeyoung Kim, Sung-Eui Yoon,
• Abstract summary: Nearest neighbor (NN) graph based visual re-ranking has emerged as a powerful approach for improving retrieval accuracy.<n>However, the effectiveness of NN graph-based re-ranking is constrained by the quality of its edge connectivity.<n>We propose a complementary denoising method based on Continuous Conditional Random Fields.
• Score: 17.05978491184936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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