论文标题
用于聚类法医图像的分析工作流程
An Analytical Workflow for Clustering Forensic Images
论文作者
论文摘要
大量图像（如果经过策划）会极大地有助于许多领域的研究质量。无监督的聚类是旨在策划此类数据集的直观但有效的一步。在这项工作中，我们提出了一个工作流程，用于无视大量法医图像集合。除了与域相关的数据之外，工作流还利用了图像的深度特征表示形式的经典聚类来将它们分组在一起。我们的手动评估显示所得群集的纯度为89 \％。
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.
