Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Voting
This work addresses the need for art historians to efficiently analyze large digitized art collections by automating object retrieval and localization, though it is incremental as it builds on existing computer vision methods adapted for art-specific challenges.
The paper tackles the problem of retrieving and localizing specific objects or motifs in large digitized art collections, which is challenging due to domain shifts from diverse artistic styles and techniques. It introduces a multi-style feature fusion approach that achieves state-of-the-art results on the Brueghel dataset and generalizes to inhomogeneous collections with many distractors, enabling fast searches in seconds.
The search for specific objects or motifs is essential to art history as both assist in decoding the meaning of artworks. Digitization has produced large art collections, but manual methods prove to be insufficient to analyze them. In the following, we introduce an algorithm that allows users to search for image regions containing specific motifs or objects and find similar regions in an extensive dataset, helping art historians to analyze large digitized art collections. Computer vision has presented efficient methods for visual instance retrieval across photographs. However, applied to art collections, they reveal severe deficiencies because of diverse motifs and massive domain shifts induced by differences in techniques, materials, and styles. In this paper, we present a multi-style feature fusion approach that successfully reduces the domain gap and improves retrieval results without labelled data or curated image collections. Our region-based voting with GPU-accelerated approximate nearest-neighbour search allows us to find and localize even small motifs within an extensive dataset in a few seconds. We obtain state-of-the-art results on the Brueghel dataset and demonstrate its generalization to inhomogeneous collections with a large number of distractors.