Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning

2024-12-18

arXiv.org

AI Summary

To address the degradation of retrieval performance caused by noisy edges—i.e., erroneous connections between dissimilar images—in neighborhood graph re-ranking, this paper proposes a graph denoising method based on Continuous Conditional Random Fields (C-CRF). It is the first to introduce C-CRF into visual re-ranking, automatically assessing edge reliability via statistical distance modeling of similarity distributions—eliminating reliance on hand-crafted thresholds. A clique potential function is designed to incorporate local structural constraints, and probabilistic inference enables end-to-end, fine-tuning-free graph optimization. Theoretical analysis and extensive experiments demonstrate strong complementarity with mainstream re-ranking techniques. When integrated with three baseline methods, the approach consistently improves mAP and Recall@k on both landmark retrieval and person re-identification benchmarks, validating its effectiveness and generalizability.

Technology Category

• Computer Vision: Image and Video Retrieval
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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).

Problem

• Noisy edges in NN graphs hinder re-ranking performance
• Incorrect connections between dissimilar images limit retrieval accuracy
• Proposing C-CRF denoising to enhance NN graph quality

Innovation

• Uses C-CRF for denoising NN graph edges
• Employs statistical distance metric for robustness
• Enhances retrieval via offline pre-processing