Food Image Recognition Based on Densely Connected Convolutional Neural Networks
Al-Selwi Metwalli, Wei Shen, Chase Q. Wu
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.