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arXiv · 1910.00100

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

Jiatong Li·Ricardo Guerrero·Vladimir Pavlovic
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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.

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Jiatong Li, Ricardo Guerrero, Vladimir Pavlovic. 2019-09-26. Deep Cooking: Predicting Relative Food Ingredient Amounts from Images. https://doi.org/10.1145/3347448.3357164

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