Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning - Algorithms, architectures, image analysis and computer graphics Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

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

Xi Shen
  • Fonction : Auteur
  • PersonId : 173126
  • IdHAL : xi-shen
Mathieu Aubry

Résumé

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.
Fichier principal
Vignette du fichier
Shen_Discovering_Visual_Patterns_in_Art_Collections_With_Spatially-Consistent_Feature_Learning_CVPR_2019_paper.pdf (4.78 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02104041 , version 1 (16-02-2021)

Identifiants

Citer

Xi Shen, Alexei A. Efros, Mathieu Aubry. Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2019, Long Beach, CA, United States. pp.9270-9279, ⟨10.1109/CVPR.2019.00950⟩. ⟨hal-02104041⟩
84 Consultations
141 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More