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An Analytical Workflow for Clustering Forensic Images
December 29, 2019 Β· Declared Dead Β· π AAAI Conference on Artificial Intelligence
"No code URL or promise found in abstract"
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Authors
Sara Mousavi, Dylan Lee, Tatianna Griffin, Dawnie Steadman, Audris Mockus
arXiv ID
2001.05845
Category
cs.CV: Computer Vision
Citations
3
Venue
AAAI Conference on Artificial Intelligence
Last Checked
5 months ago
Abstract
Large collections of images, if curated, drastically contribute to the quality of research in many domains. Unsupervised clustering is an intuitive, yet effective step towards curating such datasets. In this work, we present a workflow for unsupervisedly clustering a large collection of forensic images. The workflow utilizes classic clustering on deep feature representation of the images in addition to domain-related data to group them together. Our manual evaluation shows a purity of 89\% for the resulted clusters.
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