Abstract
To improve Asian food image classification accuracy, a method that combined Convolutional Block Attention Module (CBAM) with the Mobile NetV2, VGG16, and ResNet50 was proposed for Asian food image classification. Additionally, we proposed to use a mixed data enhancement algorithm (Mixup) to have a smoother discrimination ability. The effects of introducing the attention mechanism (CBAM) and using the mixed data enhancement algorithm (Mixup) were shown respectively through experimental comparison. The combination of these two and the final test set Top-1 accuracy rate reached 87.33%. Moreover, the information emphasized by CBAM was reflected through the visualization of the heat map. The results confirmed the classification method’s effectiveness and provided new ideas that improved Asian food image classification accuracy.
Highlights
A good diet provides humans with nutrients needed by the body making it a basis for human survival
To improve Asian food image classification accuracy, a method that combined Convolutional Block Attention Module (CBAM) with the Mobile NetV2, VGG16, and ResNet50 was proposed for Asian food image classification
The commonly used food image classification methods include the use of traditional machine learning methods and deep learning methods
Summary
A good diet provides humans with nutrients needed by the body making it a basis for human survival. Marc Bolaños and others proposed a food image recognition algorithm The core of this algorithm is first to analyze the food feature map of the input food picture, and use it to predict the kind of the input food by looking for the most similar food features. Shulin Yang et al proposed a method of performing pairwise statistics on the feature relationship between different components of food and analyzing the statistical information to come up with a classifier for food identification. It is usually difficult to accurately express the real meaning of the picture, which results in low classification accuracy This method is based on deep learning. Taskiran and others used the Food 101 data set to train models such as MobileNet, VGG, and ResNet and proposed a method of comparing correlation coefficients within categories to solve the problem of in-
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