Animal Fiber Identification under the Open Set Condition

Oliver Rippel, Sergen Gülçelik, Khosrow Rahimi, Juliana Kurniadi, Andreas Herrmann, Dorit Merhof

2022

Abstract

Animal fiber identification is an essential aspect of fabric production, since specialty fibers such as cashmere are often targeted by adulteration attempts. Proposed, automated solutions can furthermore not be applied in practice (i.e. under the open set condition), as they are trained on a small subset of all existing fiber types only and simultaneously lack the ability to reject fiber types unseen during training at test time. In our work, we overcome this limitation by applying out-of-distribution (OOD)-detection techniques to the natural fiber identification task. Specifically, we propose to jointly model the probability density function of in-distribution data across feature levels of the trained classification network by means of Gaussian mixture models. Moreover, we extend the open set F-measure to the so-called area under the open set precision-recall curve (AUPRos), a threshold-independent measure of joint in-distribution classification & OOD-detection performance for OOD-detection methods with continuous OOD scores. Exhaustive comparison to the state of the art reveals that our proposed approach performs best overall, achieving highest area under the class-averaged, open set precision-recall curve (AUPRos,avg). We thus show that the application of automated fiber identification solutions under the open set condition is feasible via OOD detection.

Paper Citation

in Harvard Style

Rippel O., Gülçelik S., Rahimi K., Kurniadi J., Herrmann A. and Merhof D. (2022). Animal Fiber Identification under the Open Set Condition. 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, SciTePress, pages 36-47. DOI: 10.5220/0010769800003124

in Bibtex Style

@conference{visapp22,
author={Oliver Rippel and Sergen Gülçelik and Khosrow Rahimi and Juliana Kurniadi and Andreas Herrmann and Dorit Merhof},
title={Animal Fiber Identification under the Open Set Condition},
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={36-47},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010769800003124},
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 (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Animal Fiber Identification under the Open Set Condition
SN - 978-989-758-555-5
AU - Rippel O.
AU - Gülçelik S.
AU - Rahimi K.
AU - Kurniadi J.
AU - Herrmann A.
AU - Merhof D.
PY - 2022
SP - 36
EP - 47
DO - 10.5220/0010769800003124
PB - SciTePress