Discovering and Analyzing Visual Structures

Project Visual Structures History Earth

Goal  |  People  |  Job offers  |  Presentations

DISCOVER is an ERC funded project (starting grant 101076028).


Our goal is to develop approaches to assist experts in identifying and analyzing patterns. Indeed, while the success of deep learning on visual data is undeniable, applications are often limited to the supervised learning scenario where the algorithm tries to infer a label for a new image based on the annotations made by experts in a reference dataset. In contrast, we want to take as input images without any annotation, automatically identify consistent patterns and model their variation and evolution, so that an expert can more easily analyze them.

The concept we will develop is the one of visual structures. Their key features will be their interpretability, in terms of correspondences, deformations, or properties of the observed images, and their ability to incorporate prior knowledge about the data and expert feedback. It will explore two complementary approaches to formally define and identify visual structures: one based on analyzing correspondences, the other on learning interpretable image models.

We will develop visual structures in two domains: historical documents and Earth imagery. For example, from temporal series of Earth images, we want to identify types of moving objects, areas with different types of vegetation or constructions, and model the evolution of their characteristics, which may correspond to changes in their activity or life cycle. Ultimately, experts will still be needed to select relevant visual structures and perform analysis, requiring to work closely with them, to design relevant features in our algorithms and adapted interfaces for interaction.


Current collaborators:

Past collaborators

Job offers

We have positions for M2/PhD, research engineer and postdoc. Please fill this form if you are interested (I will not answer e-mail applications).

Selected presentations

  • Symposium on Advances in applied mathematics and learning approaches for cultural heritage and the arts, April 22-24th, 2024 , Cagliari, Italy, invited presentation on Analysis-by-synthesis for History (slides, website)
  • Workshop on Computing the Early Modern Printed Page, invited presentation on Interpretable computer vision analysis of historical prints (slides, program)
  • Big Historical data conference, Nov 22-25th, 2023, Jena, Germany. Remote presentation on Discovering and analyzing visual patterns in historical image databases (slides, website)
  • Conference on artificIAl Intelligence and applied MAthematics for History and Archaeology, Nov 27-28th, 2023, Nice, France. Keynote presentation on Computer Vision for Historians (slides, website)
  • Machine Learning for Geometry workshop, Oct. 26th, 2023, Paris, France. Invited presentation on 3D analysis-by-synthesis (slides, website)

Website stolen from Jon Barron.