The Library as Data: Exploring Digital Collections through Machine Learning New
Recent advances in machine learning are allowing computer vision and humanities researchers to develop new tools and methods for exploring digital image collections. Neural network models are now able to match, differentiate and classify images at scale in ways which would have been impossible a few years ago. This session introduces the IIIF image data framework, which has been developed by a consortium of the world’s leading research libraries and image repositories, and demonstrates a range of different machine learning- based methods for exploring digital image collections. We will also discuss some of the ethical challenges of applying computer vision algorithms to cultural and historical image collections. Topics covered will include:
- Unlocking image collections with the IIIF image data framework
- Machine Learning: a very short introduction
- Working with images at scale: ethical and methodological challenges
- Applying computer vision methods to digital collections
PhD Students and Staff
Events available