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 problem, we introduce a multi-style feature aggregation that projects images into the same distribution, leading to more accurate and style-invariant search results. Our retrieval system is based on a voting procedure combined with fast nearest-neighbor search and enables finding and localizing motifs within an extensive image collection in seconds. The presented approach significantly improves the state-of-the-art in terms of accuracy and search time on various datasets and applies to large and inhomogeneous collections. In addition to the search algorithm, we introduce a user interface that allows art historians to apply our algorithm in practice. The interface enables users to search for single regions, multiple regions regarding different connection types and holds an interactive feedback system to improve retrieval results further. With our methodological contribution and easy-to-use user interface, this work manifests further progress towards a computer-based analysis of visual art.
Highlights
A central task of art history is to analyze the similarities between artworks to study reception processes, morphological variations of motifs or contextual changes in artworks, gaining more insight into artistic relationships
We have presented an instance retrieval algorithm and a comprehensive user interface to find and localize similar or identical regions in an extensive digital art collection
Thereby, we do not need strong supervision in terms of labeled image pairs or curated datasets for self-supervised training compared to previous approaches
Summary
A central task of art history is to analyze the similarities between artworks to study reception processes, morphological variations of motifs or contextual changes in artworks, gaining more insight into artistic relationships. To investigate these visual similarities, art historians have to find the connection between specific image regions containing motifs or meaningful objects [1, 2]. This is important for the study of iconographic questions, where.
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