loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Daniel Lehmann and Marc Ebner

Affiliation: Institut für Mathematik und Informatik, Universität Greifswald Walther-Rathenau-Straße 47, 17489 Greifswald, Germany

Keyword(s): Undersampling, Clustering, CNN.

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.142.98.108

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 493-500. DOI: 10.5220/0010841100003124

@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 (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={493-500},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010841100003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Subclass-based Undersampling for Class-imbalanced Image Classification
SN - 978-989-758-555-5
IS - 2184-4321
AU - Lehmann, D.
AU - Ebner, M.
PY - 2022
SP - 493
EP - 500
DO - 10.5220/0010841100003124
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic