  Food image recognition is one of the promising applications of visual object
recognition in computer vision. In this study, a small-scale dataset consisting
of 5822 images of ten categories and a five-layer CNN was constructed to
recognize these images. The bag-of-features (BoF) model coupled with support
vector machine (SVM) was first evaluated for image classification, resulting in
an overall accuracy of 56%; while the CNN model performed much better with an
overall accuracy of 74%. Data augmentation techniques based on geometric
transformation were applied to increase the size of training images, which
achieved a significantly improved accuracy of more than 90% while preventing
the overfitting issue that occurred to the CNN based on raw training data.
Further improvements can be expected by collecting more images and optimizing
the network architecture and hyper-parameters.
