Subclass-based Undersampling for Class-imbalanced Image Classification

Daniel Lehmann, Marc Ebner

2022

Abstract

Image classification problems are often class-imbalanced in practice. Such a class imbalance can negatively affect the classification performance of CNN models. A State-of-the-Art (SOTA) approach to address this issue is to randomly undersample the majority class. However, random undersampling can result in an information loss because the randomly selected samples may not come from all distinct groups of samples of the class (subclasses). In this paper, we examine an alternative undersampling approach. Our method undersamples a class by selecting samples from all subclasses of the class. To identify the subclasses, we investigated if clustering of the high-level features of CNN models is a suitable approach. We conducted experiments on 2 real-world datasets. Their results show that our approach can outperform a) models trained on the imbalanced dataset and b) models trained using several SOTA methods addressing the class imbalance.

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Paper Citation


in Harvard Style

Lehmann D. and Ebner M. (2022). Subclass-based Undersampling for Class-imbalanced Image Classification. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-555-5, pages 493-500. DOI: 10.5220/0010841100003124


in Bibtex Style

@conference{visapp22,
author={Daniel Lehmann and Marc Ebner},
title={Subclass-based Undersampling for Class-imbalanced Image Classification},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={493-500},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010841100003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Subclass-based Undersampling for Class-imbalanced Image Classification
SN - 978-989-758-555-5
AU - Lehmann D.
AU - Ebner M.
PY - 2022
SP - 493
EP - 500
DO - 10.5220/0010841100003124