ML Anthology

An Analytical Workflow for Clustering Forensic Images (Student Abstract)

Sara Mousavi, Dylan Lee, Tatianna Griffin, Dawnie W. Steadman, Audris Mockus
AAAI 2020 pp. 13879-13880
doi:10.1609/AAAI.V34I10.7212 /aaai/2020/mousavi2020aaai-analytical/

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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Text

Mousavi et al. "An Analytical Workflow for Clustering Forensic Images (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7212

Markdown

[Mousavi et al. "An Analytical Workflow for Clustering Forensic Images (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/mousavi2020aaai-analytical/) doi:10.1609/AAAI.V34I10.7212

BibTeX

@inproceedings{mousavi2020aaai-analytical,
  title     = {{An Analytical Workflow for Clustering Forensic Images (Student Abstract)}},
  author    = {Mousavi, Sara and Lee, Dylan and Griffin, Tatianna and Steadman, Dawnie W. and Mockus, Audris},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13879-13880},
  doi       = {10.1609/AAAI.V34I10.7212},
  url       = {https://mlanthology.org/aaai/2020/mousavi2020aaai-analytical/}
}