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Bossard, L., Guillaumin, M. and Gool, L.V. (2014) Food-101-Mining Discriminative Components with Random Forests. In: European Conference on Computer Vision (ECCV), Springer, Cham, 446-461.
https://doi.org/10.1007/978-3-319-10599-4_29

has been cited by the following article:

• TITLE: Asian Food Image Classification Based on Deep Learning

  AUTHORS: Bing Xu, Xiaopei He, Zhijian Qu

  KEYWORDS: Asian Food, Image Classification, Convolutional Neural Network, Attention Mechanism, Data Enhancement

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.3, March 3, 2021

  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.