  Finding efficient means of fingerprinting microstructural information is a
critical step towards harnessing data-centric machine learning approaches. A
statistical framework is systematically developed for compressed
characterisation of a population of images, which includes some classical
computer vision methods as special cases. The focus is on materials
microstructure. The ultimate purpose is to rapidly fingerprint sample images in
the context of various high-throughput design/make/test scenarios. This
includes, but is not limited to, quantification of the disparity between
microstructures for quality control, classifying microstructures, predicting
materials properties from image data and identifying potential processing
routes to engineer new materials with specific properties. Here, we consider
microstructure classification and utilise the resulting features over a range
of related machine learning tasks, namely supervised, semi-supervised, and
unsupervised learning.
  The approach is applied to two distinct datasets to illustrate various
aspects and some recommendations are made based on the findings. In particular,
methods that leverage transfer learning with convolutional neural networks
(CNNs), pretrained on the ImageNet dataset, are generally shown to outperform
other methods. Additionally, dimensionality reduction of these CNN-based
fingerprints is shown to have negligible impact on classification accuracy for
the supervised learning approaches considered. In situations where there is a
large dataset with only a handful of images labelled, graph-based label
propagation to unlabelled data is shown to be favourable over discarding
unlabelled data and performing supervised learning. In particular, label
propagation by Poisson learning is shown to be highly effective at low label
rates.
