Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Voting

TL;DR

This paper introduces a novel deep multi-style feature fusion algorithm for efficient object retrieval and localization in large, diverse art collections, aiding art historians in analyzing extensive digitized datasets.

Contribution

It presents a multi-style feature fusion method that reduces domain gaps and improves retrieval accuracy without labeled data, along with a GPU-accelerated voting scheme for fast localization.

Findings

Achieved state-of-the-art results on the Brueghel dataset.

Demonstrated effective generalization to large, diverse collections.

Enabled rapid localization of small motifs within extensive datasets.

Abstract

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.