Yet another digital surrogate? Computer vision and the future of collections management systems

Yet another digital surrogate? Computer vision and the future of collections management systems,
Matthew Lincoln, Research Software Engineer, Carnegie Mellon University Libraries

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Abstract
The collection catalog – and today, the museum collection database – has been the foundational tool for art history for almost two centuries. Despite enormous changes over this time in our theoretical approach to the discipline, basic cataloging of objects (who created them, how and when they were made, what concepts they illustrate or formal properties they exhibit, where they have been displayed, and who has seen them) is essential to virtually all of our historical argumentation. Whether we are using these descriptions as evidence in historical argument, or challenging and replacing them with new historical contexts, art cataloging remains central. Data-based approaches to art history, including computer vision, may make description even more central, with its focus on iconography, style and formalism, pose analysis, etc.

What could computational art history outputs like trained models or image vectors do for the field if they could be first-class digital objects within museum collections databases, joining the digital surrogates, conservation and provenance records, and scholarly bibliographies already curated there? In this talk I will discuss recent work on a prototype project to integrate simple machine learning mechanisms into collections management for the Carnegie Mellon University photo archives, and what its user interfaces and back-end architecture suggest for future CMS designs. Such a shift could help transform museum computer vision experiments from fun toys into deep research and exploration tools, massively magnifying our research impact in the real world and transforming the experience of both expert and casual users of these systems.

Biography
Dr. Matthew Lincoln is a research software engineer at Carnegie Mellon University Libraries, where he focuses on computational approaches to the study of history and culture, and on making library and archives collections tractable for data-driven research. His current book project with Getty Publications, co-authored with Dr. Sandra van Ginhoven and supported by a grant from the Samuel H. Kress Foundation, uses data-driven modeling, network analysis, and textual analysis to mine the Getty Provenance Index Databases for insights into the history of collecting and the art market. He was previously a data research specialist at the Getty Research Institute, a curatorial fellow with the National Gallery of Art, and the inaugural honorary fellow in digital art history at the Frick Art Reference Library. He has been a recipient of awards from the Getty Foundation, the Association for Computers and the Humanities, and the Alliance of Digital Humanities Organizations. He was the lead technical editor of The Programming Historian from 2017-2020.