Uploaded October 2020 | Updated September 2026, 1 day ago
A major challenge faced by curators of art such as the Metropolitan (Met) Museum of Art is how to engage users in the digital age. The Met wants to redefine how its users interact and experience its art by giving users the ability to refine and filter works of art for example based on the subject of interest regardless of the era, medium, and time. The Met is seeking to leverage machine learning and data science to determine the best method/process for predictive fine-grain attribute categorization of The Met Open Access collection. In this presentation, we show preliminary results from a Deep Learning image analysis platform for automated data labeling using art objects from the Open Access Met collection. We also aim to use data mining methods such as clustering to explore expected and unexpected commonalities in art object themes.
A major challenge faced by curators of art such as the Metropolitan (Met) Museum of Art is how to engage users in the digital age. The Met wants to redefine how its users interact and experience its art by giving users the ability to refine and filter works of art for example based on the subject of interest regardless of the era, medium, and time. The Met is seeking to leverage machine learning and data science to determine the best method/process for predictive fine-grain attribute categorization of The Met Open Access collection. In this presentation, we show preliminary results from a Deep Learning image analysis platform for automated data labeling using art objects from the Open Access Met collection. We also aim to use data mining methods such as clustering to explore expected and unexpected commonalities in art object themes.
