Vision Transformer Representations for Efficient Content-Based Image Retrieval

doi:10.1007/978-3-032-03708-4_11

Vision Transformer Representations for Efficient Content-Based Image Retrieval

2026-01-01

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DOI：10.1007/978-3-032-03708-4_11

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Content-based image retrieval (CBIR) is one of the basic tasks of computer vision. Numerous studies have been conducted, leading to many groundbreaking methods based on deep neural networks and even more recently on vision transformers (ViT). In this article, we propose a new CBIR method based on the original self-distilled with no labels semantic features (DINO), obtained using ViT, and then additionally compressed using the principal and neighbourhood component analysis. We show highly accurate results on non trivial datasets such as Caltech-256, as well as on histopathological scans such as Kather and BreaKHis. Our method freely compares with the best CBIR approaches while having very compact image representations.

Keywords:

content-based image retrieval

ViT

DINO

histopathology

component analysis

colorectal cancer

breast cancer

BreaKHis

Vision Transformer Representations for Efficient Content-Based Image Retrieval

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