Abstract
The article discusses the new digital research methodologies to investi-gate the concept of image similarity, taking as a case study the Lyon16ci database project.
Developed in collaboration with the Visual Geometry Group at the University of Oxford, this project investigates how AI-driven image recognition can enhance scholarly analysis of visual material in the hu-manities. The focus is on the use of VISE software, designed to auto-matically retrieve visually similar images based on geometric and com-positional features. The article provides a critical evaluation of the strengths and limitations of this tool in the context of art historical and visual culture research. It discusses how VISE facilitates new interpre-tative approaches by uncovering visual relationships and how it can ef-fectively enhance traditional comparative methods. The author provides an overview of the advantages and constraints of using the VISE AI software to automatically retrieve similar images, presenting some of the theoretical considerations and the research possibilities provided by image recognition tools. The Lyon16ci case offers insights into the broader potential of machine vision in redefining the scope and scale of image-based humanities research.