TY - GEN
T1 - Food Image Recognition Based on Densely Connected Convolutional Neural Networks
AU - Metwalli, Al Selwi
AU - Shen, Wei
AU - Wu, Chase Q.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Convolutional neural networks have been widely used for image recognition as they are capable of extracting features with high accuracy. In this paper, we propose a DenseFood model based on densely connected convolutional neural network architecture, which consists of multiple layers. A combination of softmax loss and center loss is used during the training process to minimize the variation within the same category and maximize the variation across different ones. For performance comparison, three models, namely, DenseFood, DenseNet121, and ResNet50 are trained using VIREO-172 dataset. In addition, we fine tune pre-trained DenseNet121 and ResNet50 models to extract features from the dataset. Experimental results show that the proposed DenseFood model achieves an accuracy of 81.23% and outperforms the other models in comparison.
AB - Convolutional neural networks have been widely used for image recognition as they are capable of extracting features with high accuracy. In this paper, we propose a DenseFood model based on densely connected convolutional neural network architecture, which consists of multiple layers. A combination of softmax loss and center loss is used during the training process to minimize the variation within the same category and maximize the variation across different ones. For performance comparison, three models, namely, DenseFood, DenseNet121, and ResNet50 are trained using VIREO-172 dataset. In addition, we fine tune pre-trained DenseNet121 and ResNet50 models to extract features from the dataset. Experimental results show that the proposed DenseFood model achieves an accuracy of 81.23% and outperforms the other models in comparison.
KW - Food image recognition
KW - convolutional neural networks
KW - deep learning
KW - image classification
UR - http://www.scopus.com/inward/record.url?scp=85084066785&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084066785&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC48513.2020.9065281
DO - 10.1109/ICAIIC48513.2020.9065281
M3 - Conference contribution
AN - SCOPUS:85084066785
T3 - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
SP - 27
EP - 32
BT - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Y2 - 19 February 2020 through 21 February 2020
ER -