Large-scale interactive retrieval in art collections using multi-style feature aggregation.

ABSTRACT: Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many research opportunities. However, manual approaches are inadequate to handle this amount of data, and it requires appropriate computer-based methods to analyze them. This article presents a visual search algorithm and user interface to support art historians to find objects and motifs in extensive datasets. Artistic image collections are subject to significant domain shifts induced by large variations in styles, artistic media, and materials. This poses new challenges to most computer vision models which are trained on photographs. To alleviate this

SUBMITTER: Ufer N 

PROVIDER: S-EPMC8612525 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature