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Authors: Bu Tianhao 1 ; Michalis Lazarou 2 and Tania Stathaki 2

Affiliations: 1 Glory Engineering & Tech Co., LTD, China ; 2 Imperial College London, U.K.

Keyword(s): Data Augmentation, Image Classification, High Boost Filtering, Edge Enhancement.

Abstract: Image classification has been a popular task due to its feasibility in real-world applications. Training neural networks by feeding them RGB images has demonstrated success over it. Nevertheless, improving the classification accuracy and computational efficiency of this process continues to present challenges that researchers are actively addressing. A widely popular embraced method to improve the classification performance of neural networks is to incorporate data augmentations during the training process. Data augmentations are simple transformations that create slightly modified versions of the training data, and can be very effective in training neural networks to mitigate overfitting and improve their accuracy performance. In this study, we draw inspiration from high-boost image filtering and propose an edge enhancement-based method as means to enhance both accuracy and training speed of neural networks. Specifically, our approach involves extracting high frequency features, suc h as edges, from images within the available dataset and fusing them with the original images, to generate new, enriched images. Our comprehensive experiments, conducted on two distinct datasets—CIFAR10 and CALTECH101, and three different network architectures—ResNet-18 ,LeNet-5 and CNN-9—demonstrate the effectiveness of our proposed method. (More)

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Paper citation in several formats:
Tianhao, B.; Lazarou, M. and Stathaki, T. (2024). Image Edge Enhancement for Effective Image Classification. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 444-451. DOI: 10.5220/0012364900003660

@conference{visapp24,
author={Bu Tianhao. and Michalis Lazarou. and Tania Stathaki.},
title={Image Edge Enhancement for Effective Image Classification},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={444-451},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012364900003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Image Edge Enhancement for Effective Image Classification
SN - 978-989-758-679-8
IS - 2184-4321
AU - Tianhao, B.
AU - Lazarou, M.
AU - Stathaki, T.
PY - 2024
SP - 444
EP - 451
DO - 10.5220/0012364900003660
PB - SciTePress