Authors:
Egor Prokopov
;
Daria Usacheva
;
Mariia Rumiantceva
and
Valeria Efimova
Affiliation:
ITMO University, Kronverkski prospect,49, Saint-Petersburg, Russia
Keyword(s):
Froth Flotation, Image Segmentation, Foundation Models, Weakly-Supervised Learning, Unsupervised Evaluation.
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.
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