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.
@article{arxiv.1910.00100,
title = {Deep Cooking: Predicting Relative Food Ingredient Amounts from Images},
author = {Jiatong Li and Ricardo Guerrero and Vladimir Pavlovic},
journal= {arXiv preprint arXiv:1910.00100},
year = {2019}
}