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

arXiv: https://arxiv.org/abs/2109.11891 · HTML: https://mlsanity.com/paper/2109.11891/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification](https://mlsanity.com/paper/1510.00745/) (2015)
- [Incorporating Intra-Class Variance to Fine-Grained Visual Recognition](https://mlsanity.com/paper/1703.00196/) (2017)
- [Deep learning for Plankton and Coral Classification](https://mlsanity.com/paper/1908.05489/) (2019)
- [On Intra-Class Variance for Deep Learning of Classifiers](https://mlsanity.com/paper/1901.11186/) (2019)

## Built on by (most cited first, 6 of 6)

- [Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers](https://mlsanity.com/paper/2305.13849/) (2023)
- [Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models](https://mlsanity.com/paper/2505.05163/) (2025)
- [Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectives](https://mlsanity.com/paper/2305.11739/) (2023)
- [Beyond Accuracy: Statistical Measures and Benchmark for Evaluation of Representation from Self-Supervised Learning](https://mlsanity.com/paper/2312.01118/) (2023)
- [How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning](https://mlsanity.com/paper/2308.08224/) (2023)
- [Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing](https://mlsanity.com/paper/2504.02335/) (2025)

## Abstract (arXiv)

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.