One of the key contribution of EnHerit is a new approach to discover copies in artworks. Many examples, code and details are available on the ArtMiner Webpage.

This project was initially developed to discover repeated details in Brueghel’s workshop artworks collected by Elizabeth Honig

We are now developing a new case study on a larger temporal and spatial scale on a large database of Venus depictions with Béatrice Joyeux-Prunel and K. Bender.

From a Computer Vision point of view, our main contribution is a new unsupervised approach to learn deep features specifically for matching spatially consistent patterns across depiction styles.

We further developed a method to compute precise displacement flows between similar artworks, giving insights into the copy process, and allowing better the copied details.


Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning,
Xi Shen, Alexei Efros and Mathieu Aubry, Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019,
PDF, Project page, code.

The Burgeoning Computer-Art Symbiosis,
Shiry Ginosar, Xi Shen, Karan Dwivedi, Elizabeth Honig, and Mathieu Aubry, XRDS: Crossroads, The ACM Magazine for Students – Computers and Art, PDF

RANSAC-Flow: generic two-stage image alignment
Xi Shen, François Darmon, Alexei Efros and Mathieu Aubry,
European Conference on Computer Vision (ECCV) 2020
PDF, webpage, code

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