An Analytical Workflow for Clustering Forensic Images (Student Abstract)

Authors

  • Sara Mousavi University of Tennessee, Knoxville
  • Dylan Lee University of Tennessee, Knoxville
  • Tatianna Griffin University of Tennessee, Knoxville
  • Dawnie Steadman University of Tennessee, Knoxville
  • Audris Mockus University of Tennessee, Knoxville

DOI:

https://doi.org/10.1609/aaai.v34i10.7212

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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Published

2020-04-03

How to Cite

Mousavi, S., Lee, D., Griffin, T., Steadman, D., & Mockus, A. (2020). An Analytical Workflow for Clustering Forensic Images (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13879-13880. https://doi.org/10.1609/aaai.v34i10.7212

Issue

Section

Student Abstract Track