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An Analytical Workflow for Clustering Forensic Images (Student Abstract)

NCJ Number
Date Published
2 pages

This article presents a workflow for unsupervised clustering a large collection of forensic images.


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. The workflow in the current project utilizes classic clustering on deep feature representation of the images in addition to domain-related data to group them together. The manual evaluation shows a purity of 89% for the resulted clusters. (Published abstract provided)


Date Published: January 1, 2020