# Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning

arXiv: https://arxiv.org/abs/1903.02678 · HTML: https://mlsanity.com/paper/1903.02678/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 26 of 26)

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- [Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Voting](https://mlsanity.com/paper/2107.06935/) (2021)
- [Image Collation: Matching illustrations in manuscripts](https://mlsanity.com/paper/2108.08109/) (2021)
- [No Annotations for Object Detection in Art through Stable Diffusion](https://mlsanity.com/paper/2412.06286/) (2024)
- [Everyone Can Be Picasso? A Computational Framework into the Myth of Human versus AI Painting](https://mlsanity.com/paper/2304.07999/) (2023)
- [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024)
- [Visual Chronicles: Using Multimodal LLMs to Analyze Massive Collections of Images](https://mlsanity.com/paper/2504.08727/) (2025)
- [ICC++: Explainable Image Retrieval for Art Historical Corpora using Image Composition Canvas](https://mlsanity.com/paper/2206.11115/) (2022)
- [Historical Printed Ornaments: Dataset and Tasks](https://mlsanity.com/paper/2408.08633/) (2024)
- [Visual Motif Identification: Elaboration of a Curated Comparative Dataset and Classification Methods](https://mlsanity.com/paper/2410.15866/) (2024)
- [Curating art exhibitions using machine learning](https://mlsanity.com/paper/2506.19813/) (2025)

## Abstract (arXiv)

Our goal in this paper is to discover near duplicate patterns in large collections of artworks. This is harder than standard instance mining due to differences in the artistic media (oil, pastel, drawing, etc), and imperfections inherent in the copying process. The key technical insight is to adapt a standard deep feature to this task by fine-tuning it on the specific art collection using self-supervised learning. More specifically, spatial consistency between neighbouring feature matches is used as supervisory fine-tuning signal. The adapted feature leads to more accurate style-invariant matching, and can be used with a standard discovery approach, based on geometric verification, to identify duplicate patterns in the dataset. The approach is evaluated on several different datasets and shows surprisingly good qualitative discovery results. For quantitative evaluation of the method, we annotated 273 near duplicate details in a dataset of 1587 artworks attributed to Jan Brueghel and his workshop. Beyond artwork, we also demonstrate improvement on localization on the Oxford5K photo dataset as well as on historical photograph localization on the Large Time Lags Location (LTLL) dataset.