Microstructure Cluster Analysis with Transfer Learning and Unsupervised Learning

We apply computer vision and machine learning methods to analyze two datasets of microstructural images. A transfer learning pipeline utilizes the fully connected layer of a pre-trained convolutional neural network as the image representation. An unsupervised learning method uses the image representations to discover visually distinct clusters of images within two datasets. A minimally supervised clustering approach classifies micrographs into visually similar groups. This approach successfully classifies images both in a dataset of surface defects in steel, where the image classes are visually distinct and in a dataset of fracture surfaces that humans have difficulty classifying. We find that the unsupervised, transfer learning method gives results comparable to fully supervised, custom-built approaches.