Development of Korean Food Image Classification Model Using Public Food Image Dataset and Deep Learning Methods

Minki Chun, Hyeonhak Jeong, Hyunmin Lee, Taewon Yoo, Hyunggu Jung

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Food image classification is useful in diet management apps for personal health management. Various methods for classifying food images in a particular country have been proposed in multiple studies. However, knowledge of Korean food image classification is limited. The objective of this study was to train a classification model for Korean food images. To train the classification model, we collected Korean food images from the AI-Hub platform, a public food image dataset. The images were pre-processed and augmented for model training. The proposed model was evaluated in an experiment using a Korean food image dataset and effectively classified Korean food images based on transfer learning. The findings of the study revealed that the model's performance in classifying food images depended on the type of food. The findings have implications for the classification model training process using CNN and the Korean public food image dataset. Future work is required to improve the performance of a classification model, especially as it pertains to its poor performance for some food image types.

Original languageEnglish
Pages (from-to)128732-128741
Number of pages10
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Keywords

  • Computer vision
  • convolutional neural networks
  • image classification
  • machine learning
  • supervised learning

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