Open AccessDevelopment of Korean Food Image Classification Model Using Public Food Image Dataset and Deep Learning Methods
Author(s) -
Minki Chun,
Hyeonhak Jeong,
Hyunmin Lee,
Taewon Yoo,
Hyunggu Jung
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3227796
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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 extant 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, a public food image dataset. The images were preprocessed and augmented for model training. The proposed model was evaluated in an experiment using a Korean food image dataset and the proposed model 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.