Food Image Recognition Based on Densely Connected Convolutional Neural Networks
Document Type
Conference Proceeding
Publication Date
2-1-2020
Abstract
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.
Identifier
85084066785 (Scopus)
ISBN
[9781728149851]
Publication Title
2020 International Conference on Artificial Intelligence in Information and Communication Icaiic 2020
External Full Text Location
https://doi.org/10.1109/ICAIIC48513.2020.9065281
First Page
027
Last Page
032
Recommended Citation
Metwalli, Al Selwi; Shen, Wei; and Wu, Chase Q., "Food Image Recognition Based on Densely Connected Convolutional Neural Networks" (2020). Faculty Publications. 5494.
https://digitalcommons.njit.edu/fac_pubs/5494
