Efficient Unsupervised Learning for Plankton Images
This work addresses the challenge of high annotation costs for plankton monitoring, offering an efficient solution for aquatic ecosystem preservation, though it is incremental as it builds on existing unsupervised and pre-trained methods.
The authors tackled the problem of classifying plankton images without manual annotation by proposing an unsupervised learning pipeline that uses a Variational Autoencoder trained on pre-trained neural network features for clustering. Their method outperformed state-of-the-art unsupervised approaches on all tested plankton datasets, providing better image embeddings.
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.