  Monitoring plankton populations in situ is fundamental to preserve the
aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor
environmental perturbations, that can reflect into consequent morphological and
dynamical modifications. Nowadays, the availability of advanced automatic or
semi-automatic acquisition systems has been allowing the production of an
increasingly large amount of plankton image data. The adoption of machine
learning algorithms to classify such data may be affected by the significant
cost of manual annotation, due to both the huge quantity of acquired data and
the numerosity of plankton species. To address these challenges, we propose an
efficient unsupervised learning pipeline to provide accurate classification of
plankton microorganisms. We build a set of image descriptors exploiting a
two-step procedure. First, a Variational Autoencoder (VAE) is trained on
features extracted by a pre-trained neural network. We then use the learnt
latent space as image descriptor for clustering. We compare our method with
state-of-the-art unsupervised approaches, where a set of pre-defined
hand-crafted features is used for clustering of plankton images. The proposed
pipeline outperforms the benchmark algorithms for all the plankton datasets
included in our analysis, providing better image embedding properties.
