Teaching a machine to see: unsupervised image segmentation and
  categorisation using growing neural gas and hierarchical clustering
  We present a novel unsupervised learning approach to automatically segment
and label images in astronomical surveys. Automation of this procedure will be
essential as next-generation surveys enter the petabyte scale: data volumes
will exceed the capability of even large crowd-sourced analyses. We demonstrate
how a growing neural gas (GNG) can be used to encode the feature space of
imaging data. When coupled with a technique called hierarchical clustering,
imaging data can be automatically segmented and labelled by organising nodes in
the GNG. The key distinction of unsupervised learning is that these labels need
not be known prior to training, rather they are determined by the algorithm
itself. Importantly, after training a network can be be presented with images
it has never 'seen' before and provide consistent categorisation of features.
As a proof-of-concept we demonstrate application on data from the Hubble Space
Telescope Frontier Fields: images of clusters of galaxies containing a mixture
of galaxy types that would easily be recognised and classified by a human
inspector. By training the algorithm using one field (Abell 2744) and applying
the result to another (MACS0416.1-2403), we show how the algorithm can cleanly
separate image features that a human would associate with early and late type
galaxies. We suggest that the algorithm has potential as a tool in the
automatic analysis and data mining of next-generation imaging and spectral
surveys, and could also find application beyond astronomy.
