TY - GEN
T1 - Thai fast food image classification using deep learning
AU - Hnoohom, Narit
AU - Yuenyong, Sumeth
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - This paper presents a prediction model for classifying Thai fast food images. The model uses a deep learning process that was trained on natural images (GoogLeNet dataset) and was fine-tuned to generate the predictive Thai fast food model. The researchers created a dataset, called the Thai Fast Food (TFF) dataset, which contained 3,960 images. The dataset was divided into eleven groups of food images comprised of omelet on rice, rice topped with stir-fried chicken and basil, barbecued red pork in sauce with rice, stewed pork leg on rice, Thai fried noodle, rice with curried chicken, steamed chicken with rice, shrimp-paste fried rice, fried noodle with pork in soy sauce and vegetables, wide rice noodles with vegetables and meat. The final group comprised dishes which are not members of the other ten groups listed (non-ten-types), but which exist among Thai fast food. The classification average accuracy on a separate test set shows that Thai fast food can be predicted at 88.33%.
AB - This paper presents a prediction model for classifying Thai fast food images. The model uses a deep learning process that was trained on natural images (GoogLeNet dataset) and was fine-tuned to generate the predictive Thai fast food model. The researchers created a dataset, called the Thai Fast Food (TFF) dataset, which contained 3,960 images. The dataset was divided into eleven groups of food images comprised of omelet on rice, rice topped with stir-fried chicken and basil, barbecued red pork in sauce with rice, stewed pork leg on rice, Thai fried noodle, rice with curried chicken, steamed chicken with rice, shrimp-paste fried rice, fried noodle with pork in soy sauce and vegetables, wide rice noodles with vegetables and meat. The final group comprised dishes which are not members of the other ten groups listed (non-ten-types), but which exist among Thai fast food. The classification average accuracy on a separate test set shows that Thai fast food can be predicted at 88.33%.
KW - Classification
KW - Deep learning
KW - Thai fast food images
UR - https://www.scopus.com/pages/publications/85050003696
U2 - 10.1109/ECTI-NCON.2018.8378293
DO - 10.1109/ECTI-NCON.2018.8378293
M3 - Conference contribution
AN - SCOPUS:85050003696
T3 - 1st International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI-NCON 2018
SP - 116
EP - 119
BT - 1st International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI-NCON 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI-NCON 2018
Y2 - 25 February 2018 through 28 February 2018
ER -