An Attempt at Metadata Enhancement through Machine Learning

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dc.contributor.authorPeters, Todd C.
dc.contributor.authorLong, Jason
dc.date.accessioned2023-04-03T15:47:50Z
dc.date.available2023-04-03T15:47:50Z
dc.date.issued2022-05
dc.description.abstractThis presentation will share what learned about machine learning and applicability to generate metadata to enhance discoverability during a pilot project. Object detection through neural networks is a rapidly developing field. Using machine learning large sets of images can be analyzed, objects detected and classified. We used the pretrained models COCO, Inception, ResNet, VGG19, and Xception to classify objects in images in our San Marcos Daily Record newspaper negative collection. Our initial use of these models did not yield usable metadata, however it did provide a useful first step into machine learning and knowledge to develop future research.
dc.description.departmentUniversity Libraries
dc.formatImage
dc.format.extent15 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationPeters, T., & Long, J. (2022). An attempt at metadata enhancement through machine learning. Presented at the Texas Conference on Digital Libraries, Austin, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/16521
dc.language.isoen
dc.sourceTexas Conference on Digital Libraries, May 2022, Austin, Texas, United States.
dc.subjectmachine learning
dc.subjectmetadata
dc.subjectdiscovery
dc.titleAn Attempt at Metadata Enhancement through Machine Learning
dc.typePresentation

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