Tackling Inter-Class Similarity and Intra-Class Variance for Microscopic Image-based Classification

TL;DR

This paper introduces a novel classification approach for microscopic aquatic microorganisms that partitions high-variance classes into sub-classes, enabling finer feature learning and improved accuracy over existing methods.

Contribution

It proposes an automatic class partitioning method based on visual features to handle intra-class variance, enhancing microscopic image classification.

Findings

Outperforms state-of-the-art methods on freshwater benthic diatoms

Effective in handling intra-class variance due to visual changes

Improves classification accuracy for aquatic microorganisms

Abstract

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.