Weak Segmentation and Unsupervised Evaluation: Application to Froth Flotation Images

Egor Prokopov, Daria Usacheva, Mariia Rumiantceva, Valeria Efimova

2025

Abstract

Images featuring clumped texture object types are prevalent across various domains, and accurate analysis of this data is crucial for numerous industrial applications, including ore flotation—a vital process for material enrichment. Although computer vision facilitates the automation of such analyses, obtaining annotated data remains a challenge due to the labor-intensive and time-consuming nature of manual labeling. In this paper, we propose a universal weak segmentation method adaptable to different clumped texture composite images. We validate our approach using froth flotation images as a case study, integrating classical watershed techniques with foundational models for weak labeling. Additionally, we explore unsupervised evaluation metrics that account for highly imbalanced class distributions. Our dataset was tested across several architectures, with Swin-UNETR demonstrating the highest performance, achieving 89% accuracy and surpassing the same model tested on other datasets. This approach highlights the potential for effective segmentation with minimal manual annotations while ensuring generalizability to other domains.

Paper Citation

in Harvard Style

Prokopov E., Usacheva D., Rumiantceva M. and Efimova V. (2025). Weak Segmentation and Unsupervised Evaluation: Application to Froth Flotation Images. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 500-507. DOI: 10.5220/0013181100003912

in Bibtex Style

@conference{visapp25,
author={Egor Prokopov and Daria Usacheva and Mariia Rumiantceva and Valeria Efimova},
title={Weak Segmentation and Unsupervised Evaluation: Application to Froth Flotation Images},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={500-507},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013181100003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Weak Segmentation and Unsupervised Evaluation: Application to Froth Flotation Images
SN - 978-989-758-728-3
AU - Prokopov E.
AU - Usacheva D.
AU - Rumiantceva M.
AU - Efimova V.
PY - 2025
SP - 500
EP - 507
DO - 10.5220/0013181100003912
PB - SciTePress