In this paper, we study the novel problem of not only predicting ingredients
from a food image, but also predicting the relative amounts of the detected
ingredients. We propose two prediction-based models using deep learning that
output sparse and dense predictions, coupled with important semi-automatic
multi-database integrative data pre-processing, to solve the problem.
Experiments on a dataset of recipes collected from the Internet show the models
generate encouraging experimental results