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Building a Visual Search Engine for Art Historians with Benoit Seguin, Computer Scientist
In an age of rapidly evolving tech facilitating a number of things from daily tasks, to communication and research, some subjects trail behind, notably, art history. Replica aims to facilitate art historical research by using machine learning and modern image searching algorithms to help art historians navigate large iconographic collections. In short, Replica aims to go beyond the limitations of search terms and keywords and use images to enable researchers to identify visual information which may not been indexed--textual queries cannot, for example, show results for similar shapes, forms, or motifs. Replica could assist researchers in identifying visual links, pin-pointing when a specific type of iconography emerged and how it has evolved over time.
In this episode, we speak with Benoit Seguin, a computer scientist helping build the Replica search engine. Seguin explains how Replica works, what the mission is, who's involved, and how it can be immensely useful to those interested in art, art history, and visual culture.
-About Replica-
The Replica project led by the DHLAB aims to build the first search engine designed specifically for the search and exploration of artistic collections (including paintings, drawings, engravings, sculpture and photography). This employs the latest state-of-the art artificial intelligence techniques, such as Deep Learning and Convolutional Neural Networks, for the search and display of information.
In partnership with the Giorgio Cini Foundation in Venice and Factum Arte in Madrid, the Replica project aims to digitize roughly one million artistic reproductions using these images to populate the new search engine and as the basis for new art historical inquiries.
-About Benoit Seguin-
Benoit Seguin is a PhD student at the Digital Humanities Laboratory (DHLAB) at EPFL. His main interests lie in Computer Vision, Machine Learning and Image Processing. Benoit's thesis is based on the Replica Project where he implements machine learning and modern image searching algorithms to help art historians navigate large, iconographic collections. Benoit received a Master of Science from EPFL and a Diplôme d’Ingénieur from École Polytechnique.
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In an age of rapidly evolving tech facilitating a number of things from daily tasks, to communication and research, some subjects trail behind, notably, art history. Replica aims to facilitate art historical research by using machine learning and modern image searching algorithms to help art historians navigate large iconographic collections. In short, Replica aims to go beyond the limitations of search terms and keywords and use images to enable researchers to identify visual information which may not been indexed--textual queries cannot, for example, show results for similar shapes, forms, or motifs. Replica could assist researchers in identifying visual links, pin-pointing when a specific type of iconography emerged and how it has evolved over time.
In this episode, we speak with Benoit Seguin, a computer scientist helping build the Replica search engine. Seguin explains how Replica works, what the mission is, who's involved, and how it can be immensely useful to those interested in art, art history, and visual culture.
-About Replica-
The Replica project led by the DHLAB aims to build the first search engine designed specifically for the search and exploration of artistic collections (including paintings, drawings, engravings, sculpture and photography). This employs the latest state-of-the art artificial intelligence techniques, such as Deep Learning and Convolutional Neural Networks, for the search and display of information.
In partnership with the Giorgio Cini Foundation in Venice and Factum Arte in Madrid, the Replica project aims to digitize roughly one million artistic reproductions using these images to populate the new search engine and as the basis for new art historical inquiries.
-About Benoit Seguin-
Benoit Seguin is a PhD student at the Digital Humanities Laboratory (DHLAB) at EPFL. His main interests lie in Computer Vision, Machine Learning and Image Processing. Benoit's thesis is based on the Replica Project where he implements machine learning and modern image searching algorithms to help art historians navigate large, iconographic collections. Benoit received a Master of Science from EPFL and a Diplôme d’Ingénieur from École Polytechnique.