  Automatic classification of aquatic microorganisms is based on the
morphological features extracted from individual images. The current works on
their classification do not consider the inter-class similarity and intra-class
variance that causes misclassification. We are particularly interested in the
case where variance within a class occurs due to discrete visual changes in
microscopic images. In this paper, we propose to account for it by partitioning
the classes with high variance based on the visual features. Our algorithm
automatically decides the optimal number of sub-classes to be created and
consider each of them as a separate class for training. This way, the network
learns finer-grained visual features. Our experiments on two databases of
freshwater benthic diatoms and marine plankton show that our method can
outperform the state-of-the-art approaches for classification of these aquatic
microorganisms.
