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Authors: Georgios Lydakis 1 ; 2 ; Iason Oikonomidis 1 ; Dimitrios Kosmopoulos 3 and Antonis A. Argyros 1 ; 2

Affiliations: 1 Computer Science Department, University of Crete, Greece ; 2 Foundation for Research and Technology - Hellas (FORTH), Greece ; 3 University of Patras, Greece

Keyword(s): Automatic Data Annotation, Noisy Annotation, Hand Posture Classification.

Abstract: The success of deep learning in recent years relies on the availability of large amounts of accurately annotated training data. In this work, we investigate a technique for utilizing automatically annotated data in classification problems. Using a small number of manually annotated samples, and a large set of data that feature automatically created, noisy labels, our approach trains a Convolutional Neural Network (CNN) in an iterative manner. The automatic annotations are combined with the predictions of the network in order to gradually expand the training set. In order to evaluate the performance of the proposed approach, we apply it to the problem of hand posture recognition from RGB images. We compare the results of training a CNN classifier with and without the use of our technique. Our method yields a significant increase in average classification accuracy, and also decreases the deviation in class accuracies, thus indicating the validity and the usefulness of the proposed appr oach. (More)

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Paper citation in several formats:
Lydakis, G.; Oikonomidis, I.; Kosmopoulos, D. and Argyros, A. (2022). Exploitation of Noisy Automatic Data Annotation and Its Application to Hand Posture 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 632-641. DOI: 10.5220/0010906200003124

@conference{visapp22,
author={Georgios Lydakis. and Iason Oikonomidis. and Dimitrios Kosmopoulos. and Antonis A. Argyros.},
title={Exploitation of Noisy Automatic Data Annotation and Its Application to Hand Posture 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={632-641},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010906200003124},
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 - Exploitation of Noisy Automatic Data Annotation and Its Application to Hand Posture Classification
SN - 978-989-758-555-5
IS - 2184-4321
AU - Lydakis, G.
AU - Oikonomidis, I.
AU - Kosmopoulos, D.
AU - Argyros, A.
PY - 2022
SP - 632
EP - 641
DO - 10.5220/0010906200003124
PB - SciTePress

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