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New framework enhances image retrieval with combined manifold learning techniques

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Researchers have developed a new framework for content-based image retrieval (CBIR) that combines projection-based and rank-based manifold learning strategies. This approach aggregates alternative low-dimensional feature representations generated by Uniform Manifold Approximation and Projection (UMAP) with re-ranked lists using the Borda Count method. Experiments using features from ResNet152, Swin Transformer, and DINOv2 models demonstrated improved retrieval effectiveness, particularly in scenarios where baseline representations struggled with precision. AI

RANK_REASON The cluster contains a research paper submitted to arXiv detailing a new method for image retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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New framework enhances image retrieval with combined manifold learning techniques

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The cluster contains a research paper submitted to arXiv detailing a new method for image retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Vinicius Atsushi Sato Kawai, Gustavo Rosseto Leticio, Lucas Pascotti Valem, Daniel Carlos Guimar\~aes Pedronette ·

    Aggregating Neighbor Embedding Projection and Rank-Based Manifold Learning for Image Retrieval

    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 …

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