Global-to-Local or Local-to-Global? Enhancing Image Retrieval with Efficient Local Search and Effective Global Re-ranking

• URL: http://arxiv.org/abs/2509.04351v2
• Date: Fri, 05 Sep 2025 11:14:38 GMT
• Title: Global-to-Local or Local-to-Global? Enhancing Image Retrieval with Efficient Local Search and Effective Global Re-ranking
• Authors: Dror Aiger, Bingyi Cao, Kaifeng Chen, Andre Araujo,
• Abstract summary: We introduce a local-to-global retrieval paradigm, where efficient local feature search meets effective global feature re-ranking.<n>We demonstrate solid retrieval performance, setting new state-of-the-art results on the Revisited Oxford and Paris datasets.
• Score: 10.523841607264414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The dominant paradigm in image retrieval systems today is to search large databases using global image features, and re-rank those initial results with local image feature matching techniques. This design, dubbed global-to-local, stems from the computational cost of local matching approaches, which can only be afforded for a small number of retrieved images. However, emerging efficient local feature search approaches have opened up new possibilities, in particular enabling detailed retrieval at large scale, to find partial matches which are often missed by global feature search. In parallel, global feature-based re-ranking has shown promising results with high computational efficiency. In this work, we leverage these building blocks to introduce a local-to-global retrieval paradigm, where efficient local feature search meets effective global feature re-ranking. Critically, we propose a re-ranking method where global features are computed on-the-fly, based on the local feature retrieval similarities. Such re-ranking-only global features leverage multidimensional scaling techniques to create embeddings which respect the local similarities obtained during search, enabling a significant re-ranking boost. Experimentally, we demonstrate solid retrieval performance, setting new state-of-the-art results on the Revisited Oxford and Paris datasets.

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