# MorphoCluster: Efficient Annotation of Plankton images by Clustering

arXiv: https://arxiv.org/abs/2005.01595 · HTML: https://mlsanity.com/paper/2005.01595/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 9 of 9)

- [Deep Learning Classification of Lake Zooplankton](https://mlsanity.com/paper/2108.05258/) (2021)
- [Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectives](https://mlsanity.com/paper/2305.11739/) (2023)
- [Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook](https://mlsanity.com/paper/2109.14737/) (2021)
- [Self-Supervised Pretraining for Fine-Grained Plankton Recognition](https://mlsanity.com/paper/2503.11341/) (2025)
- [Efficient Unsupervised Learning for Plankton Images](https://mlsanity.com/paper/2209.06726/) (2022)
- [Label Smarter, Not Harder: CleverLabel for Faster Annotation of Ambiguous Image Classification with Higher Quality](https://mlsanity.com/paper/2305.12811/) (2023)
- [Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation](https://mlsanity.com/paper/2306.12189/) (2023)
- [Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy](https://mlsanity.com/paper/2110.06630/) (2021)
- [SuoiAI: Building a Dataset for Aquatic Invertebrates in Vietnam](https://mlsanity.com/paper/2504.15252/) (2025)

## Abstract (arXiv)

In this work, we present MorphoCluster, a software tool for data-driven, fast and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2M objects into 280 data-driven classes in 71 hours (16k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate and consistent, provides a fine-grained and data-driven classification and enables novelty detection. MorphoCluster is available as open-source software at https://github.com/morphocluster.