@inproceedings{e20ba0d0b509468fa93eeaa59e674755,
title = "Deep cooking: Predicting relative food ingredient amounts from images",
abstract = "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.",
keywords = "Amount estimation, Datasets, Ingredients, Neural networks",
author = "Jiatong Li and Ricardo Guerrero and Vladimir Pavlovic",
year = "2019",
month = oct,
day = "15",
doi = "10.1145/3347448.3357164",
language = "English (US)",
series = "MADiMa 2019 - Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management, co-located with MM 2019",
publisher = "Association for Computing Machinery, Inc",
pages = "2--6",
booktitle = "MADiMa 2019 - Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management, co-located with MM 2019",
note = "5th International Workshop on Multimedia Assisted Dietary Management, MADiMa 2019, held with ACM Multimedia 2019 ; Conference date: 21-10-2019",
}