Proceedings of the 2024 3rd International Conference on Educational Science and Social Culture (ESSC 2024)

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Impact of Noise and Distortion on ResNet50-Based Image Feature Extraction in E-commerce

Authors
Xianfeng Shang1, *
1The Experimental High School Attached to Beijing Normal University, Beijing, 100032, China
*Corresponding author. Email: xianfengkenzify@gmail.com
Corresponding Author
Xianfeng Shang
Available Online 3 April 2025.
DOI
10.2991/978-2-38476-384-9_103How to use a DOI?
Keywords
E-commerce; image feature extraction; ResNet50; noise; distortion
Abstract

In a dynamic e-commerce environment, robust image feature extraction is crucial for many applications. This study explores the performance of a ResNet50-based image feature extraction model on various e-commerce product image datasets. The researchers systematically evaluate the model’s resilience under different noise conditions and distortion types, simulating real-world challenges. The findings reveal a significant negative correlation between the level of noise/distortion and the model’s accuracy, highlighting the detrimental effects of image degradation. Furthermore, the ResNet50-based model strikes a good balance between accuracy and computational complexity. This research provides valuable insights into the practical impact of noise and distortion in e-commerce image analysis and emphasizes the importance of developing robust feature extraction models for real-world applications.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 3rd International Conference on Educational Science and Social Culture (ESSC 2024)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
3 April 2025
ISBN
978-2-38476-384-9
ISSN
2352-5398
DOI
10.2991/978-2-38476-384-9_103How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xianfeng Shang
PY  - 2025
DA  - 2025/04/03
TI  - Impact of Noise and Distortion on ResNet50-Based Image Feature Extraction in E-commerce
BT  - Proceedings of the 2024 3rd International Conference on Educational Science and Social Culture (ESSC 2024)
PB  - Atlantis Press
SP  - 914
EP  - 920
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-384-9_103
DO  - 10.2991/978-2-38476-384-9_103
ID  - Shang2025
ER  -