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Authors: Michał Koziarski ; Bogusław Cyganek and Kazimierz Wiatr

Affiliation: AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland, Academic Computer Center Cyfronet AGH, Ul. Nawojki 11, 30-950 Kraków, Poland

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): Imbalanced Data Classification, Small-Scale Image Recognition, Convolutional Neural Networks, Feature Representation, MobileNet.

Abstract: Data imbalance remains one of the most wide-spread challenges in the contemporary machine learning. Presence of imbalanced data can affect the learning possibility of most traditional classification algorithms. One of the the strategies for handling data imbalance are data-level algorithms that modify the original data distribution. However, despite the amount of existing methods, most are ill-suited for handling image data. One of the possible solutions to this problem is using alternative feature representations, such as high-level features extracted from convolutional layers of a neural network. In this paper we experimentally evaluate the possibility of using both the high-level features, as well as the original image representation, on several popular benchmark datasets with artificially introduced data imbalance. We examine the impact of different data-level algorithms on both strategies, and base the classification on MobileNet neural architecture. Achieved results indicate tha t despite their theoretical advantages, high-level features extracted from a pretrained neural network result in a worse performance than end-to-end image classification. (More)

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Paper citation in several formats:
Koziarski, M.; Cyganek, B. and Wiatr, K. (2020). The Choice of Feature Representation in Small-Scale MobileNet-Based Imbalanced Image Recognition. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-402-2; ISSN 2184-4321, pages 633-638. DOI: 10.5220/0009357206330638

@conference{visapp20,
author={Michał Koziarski. and Bogusław Cyganek. and Kazimierz Wiatr.},
title={The Choice of Feature Representation in Small-Scale MobileNet-Based Imbalanced Image Recognition},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2020},
pages={633-638},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009357206330638},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - The Choice of Feature Representation in Small-Scale MobileNet-Based Imbalanced Image Recognition
SN - 978-989-758-402-2
IS - 2184-4321
AU - Koziarski, M.
AU - Cyganek, B.
AU - Wiatr, K.
PY - 2020
SP - 633
EP - 638
DO - 10.5220/0009357206330638

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