Szczegóły publikacji
Opis bibliograficzny
Vision transformer representations for efficient content-based image retrieval / Stanisław ŁAŻEWSKI, Bogusław CYGANEK // W: Artificial Intelligence and Soft Computing : 24th International Conference, ICAISC 2025 : Zakopane, Poland, June 22–26, 2025 : proceedings , Pt. 2 / eds. Leszek Rutkowski, [et al.]. — Cham : Springer Nature Switzerland, cop. 2026. — ( Lecture Notes in Computer Science ; ISSN 0302-9743. Lecture Notes in Artificial Intelligence ; 15949 ). — ISBN: 978-3-032-03707-7; e-ISBN: 978-3-032-03708-4. — S. 144–157. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-11-01
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164441 |
|---|---|
| Data dodania do BaDAP | 2026-01-22 |
| DOI | 10.1007/978-3-032-03708-4_11 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Artificial Intelligence and Soft Computing 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
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.
