English(EN) Aggregating Neighbor Embedding Projection and Rank-Based Manifold Learning for Image Retrieval

新框架结合流形学习技术增强图像检索能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员开发了一个新的内容基础图像检索（CBIR）框架，该框架结合了基于投影和基于排名的流形学习策略。该方法将均匀流形逼近与投影（UMAP）生成的替代低维特征表示与使用Borda Count方法重新排序的列表进行聚合。使用ResNet152、Swin Transformer和DINOv2模型的特征进行的实验证明了检索效果的提高，特别是在基线表示在精度方面遇到困难的情况下。 AI

排序理由该集群包含一篇提交到arXiv的研究论文，详细介绍了一种新的图像检索方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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Signal score: 21 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇提交到arXiv的研究论文，详细介绍了一种新的图像检索方法。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.CV TIER_1 English(EN) · Vinicius Atsushi Sato Kawai, Gustavo Rosseto Leticio, Lucas Pascotti Valem, Daniel Carlos Guimar\~aes Pedronette · 2026-09-03 04:00

聚合邻域嵌入投影与基于排名的流形学习用于图像检索

arXiv:2609.01963v1 Announce Type: new Abstract: Content-based image retrieval (CBIR) has advanced significantly with deep learning, yet effectively ranking similar images remains challenging, particularly in high-dimensional feature spaces, where pairwise distances often fail to …

本文如何被排名

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聚合邻域嵌入投影与基于排名的流形学习用于图像检索

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