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Fashion Category Oversampling Automation System

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(1), pp.31-40
  • DOI : 10.9708/jksci.2024.29.01.031
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 24, 2023
  • Accepted : December 26, 2023
  • Published : January 31, 2024

Minsun Yeu 1 Do Hyeok Yoo 2 SuJin Bak 2

1울산대학교
2차세대융합기술연구원

Accredited

ABSTRACT

In the realm of domestic online fashion platform industry the manual registration of product information by individual business owners leads to inconvenience and reliability issues, especially when dealing with simultaneous registrations of numerous product groups. Moreover, bias is significantly heightened due to the low quality of product images and an imbalance in data quantity. Therefore, this study proposes a ResNet50 model aimed at minimizing data bias through oversampling techniques and conducting multiple classifications for 13 fashion categories. Transfer learning is employed to optimize resource utilization and reduce prolonged learning times. The results indicate improved discrimination of up to 33.4% for data augmentation in classes with insufficient data compared to the basic convolution neural network (CNN) model. The reliability of all outcomes is underscored by precision and affirmed by the recall curve. This study is suggested to advance the development of the domestic online fashion platform industry to a higher echelon.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.