Research:Image recognition of painting subject matter
Students in the School of Data Science at the University of Virginia used machine learning techniques in image recognition to topic tag paintings in the collection of the Metropolitan Museum of art.
Identify the subject matter of paintings, including details such as recognizable elements and features
Sample images are in commons:Category:Paintings in the Metropolitan Museum of Art and here:
The Musicians Caravaggio
The Fortune Teller de La Tour
The Horse Fair, Rosa Bonheur
A Girl Asleep, Vermeer
Golden Cock and Hen, unknown
Pity, William Blake
create metadata for what paintings depict; add this to Wikidata "depicts"statements (d:Property:P180) and integrate output into "structured data on Commons", in addition to producing a standalone dataset
- Late August 2019
- Students select research projects from an available pool
- Late September 2019
- Proposal presentation
- May 2020
- Project ends
- Data management in Wikimedia projects
- https://dumps.wikimedia.org/, "A complete copy of all Wikimedia wikis, in the form of wikitext source and metadata embedded in XML."
- d:Wikidata:Data access
- d:Wikidata:How to use data on Wikimedia projects
- Research:Quarry, a tool with a support community which could assist with presenting the list of users who received a block
- Metropolitan Museum of Art digital media
- Tallon, Loic (7 February 2017). "Introducing Open Access at The Met". Metropolitan Museum of Art.
- Knipel, Richard (7 February 2017). "The Metropolitan Museum of Art makes 375,000 images of public domain art freely available under Creative Commons Zero – Wikimedia Blog". Wikimedia Blog.
- "Open Access at The Met". www.metmuseum.org. Metropolitan Museum of Art.
- "Image and Data Resources - Open Access Policy". www.metmuseum.org.
- Chen, Amanda (20 December 2017). "A Deep Dive into The Met’s Collection Information Digital Work System". Medium.
- In Wikimedia projects
- Similar efforts
- wmania:2019:Research/Discovering and Analyzing Wikimedia Images
- Projects of , research scientist at Wikimedia Foundation
- 2019 tutorial on GitHub
- commons:Commons:Structured data
- Surapaneni, Sudeepti; Syed, Sana; Lee, Logan Yoonhyuk (24 April 2020). "Exploring Themes and Bias in Art using Machine Learning Image Analysis". 2020 Systems and Information Engineering Design Symposium (SIEDS) (IEEE). doi:10.1109/SIEDS49339.2020.9106656.
- University of Virginia School of Data Science (3 April 2020). "Creating a Global Museum: UVA School of Data Science 2020 Capstone Project". Vimeo. University of Virginia School of Data Science.
- Evett, Meg (13 April 2020). "Creating a Global Museum of Art". School of Data Science. University of Virginia.