Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning
This addresses the challenge of pattern discovery in artworks for art historians and digital humanities, but it is incremental as it adapts existing methods to a specific domain.
The paper tackled the problem of discovering near duplicate patterns in large art collections by fine-tuning a deep feature with self-supervised learning using spatial consistency, resulting in more accurate style-invariant matching and surprisingly good qualitative results, with quantitative evaluation on 273 annotated details in 1587 artworks.
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.