@Article{cmc.2020.06508,
AUTHOR = {Lili Pan, Cong Li, Samira Pouyanfar, Rongyu Chen, Yan Zhou},
TITLE = {A Novel Combinational Convolutional Neural Network for Automatic Food-Ingredient Classification},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {2},
PAGES = {731--746},
URL = {http://www.techscience.com/cmc/v62n2/38273},
ISSN = {1546-2226},
ABSTRACT = {With the development of deep learning and Convolutional Neural Networks 
(CNNs), the accuracy of automatic food recognition based on visual data have 
significantly improved. Some research studies have shown that the deeper the model is, 
the higher the accuracy is. However, very deep neural networks would be affected by the 
overfitting problem and also consume huge computing resources. In this paper, a new 
classification scheme is proposed for automatic food-ingredient recognition based on 
deep learning. We construct an up-to-date combinational convolutional neural network 
(CBNet) with a subnet merging technique. Firstly, two different neural networks are 
utilized for learning interested features. Then, a well-designed feature fusion component 
aggregates the features from subnetworks, further extracting richer and more precise 
features for image classification. In order to learn more complementary features, the 
corresponding fusion strategies are also proposed, including auxiliary classifiers and 
hyperparameters setting. Finally, CBNet based on the well-known VGGNet, ResNet and 
DenseNet is evaluated on a dataset including 41 major categories of food ingredients and 
100 images for each category. Theoretical analysis and experimental results demonstrate 
that CBNet achieves promising accuracy for multi-class classification and improves the 
performance of convolutional neural networks.},
DOI = {10.32604/cmc.2020.06508}
}