Robust deep unsupervised learning framework to discover unseen plankton species

Eivind Salvesen; Aya Saad; Annette Stahl

doi:10.1117/12.2622489

4 March 2022 Robust deep unsupervised learning framework to discover unseen plankton species

Eivind Salvesen, Aya Saad, Annette Stahl

Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 120840V (2022) https://doi.org/10.1117/12.2622489
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy

Abstract

Deep convolutional neural networks have proven effective in computer vision, especially in the task of image classification Nevertheless, the success is limited to supervised learning approaches, requiring extensive amounts of labeled training data that impose time-consuming manual efforts. Unsupervised deep learning methods were introduced to overcome this challenge. The gap, however, towards achieving comparable classification accuracy to supervised learning is still significant. This paper presents a deep learning framework for images of planktonic organisms with no ground truth or manually labeled data. This work combines feature extraction methods using state-of-the-art unsupervised training schemes with clustering algorithms to minimize the labeling effort while improving the classification process based on essential features learned by the deep learning model. The models utilized in the framework are tested over existing planktonic data sets. Empirical results show that unsupervised approaches that cluster the data based on the deep learning model’s feature space representations improve the classification task and can identify classes that have not been seen during the learning process.

Citation Download Citation

Eivind Salvesen, Aya Saad, and Annette Stahl "Robust deep unsupervised learning framework to discover unseen plankton species", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 120840V (4 March 2022); https://doi.org/10.1117/12.2622489

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KEYWORDS

Feature extraction

Data modeling

Machine learning

Expectation maximization algorithms

Performance modeling

Computer programming

Image classification

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