# Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1802.09670 · HTML: https://mlsanity.com/paper/1802.09670/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 17 of 17)

- [An End-to-End Food Image Analysis System](https://mlsanity.com/paper/2102.00645/) (2021)
- [Towards Learning Food Portion From Monocular Images With Cross-Domain Feature Adaptation](https://mlsanity.com/paper/2103.07562/) (2021)
- [An Integrated System for Mobile Image-Based Dietary Assessment](https://mlsanity.com/paper/2110.01754/) (2021)
- [An End-to-end Food Portion Estimation Framework Based on Shape Reconstruction from Monocular Image](https://mlsanity.com/paper/2308.01810/) (2023)
- [Image Based Food Energy Estimation With Depth Domain Adaptation](https://mlsanity.com/paper/2208.12153/) (2022)
- [DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion](https://mlsanity.com/paper/2310.11702/) (2023)
- [A review on vision-based analysis for automatic dietary assessment](https://mlsanity.com/paper/2108.02947/) (2021)
- [An Improved Encoder-Decoder Framework for Food Energy Estimation](https://mlsanity.com/paper/2309.00468/) (2023)
- [Saliency-Aware Class-Agnostic Food Image Segmentation](https://mlsanity.com/paper/2102.06882/) (2021)
- [Towards the Creation of a Nutrition and Food Group Based Image Database](https://mlsanity.com/paper/2206.02086/) (2022)
- [Diffusion Model with Clustering-based Conditioning for Food Image Generation](https://mlsanity.com/paper/2309.00199/) (2023)
- [Improving Food Detection For Images From a Wearable Egocentric Camera](https://mlsanity.com/paper/2301.07861/) (2023)
- [Comprehensive Evaluation of Large Multimodal Models for Nutrition Analysis: A New Benchmark Enriched with Contextual Metadata](https://mlsanity.com/paper/2507.07048/) (2025)
- [Food Portion Estimation: From Pixels to Calories](https://mlsanity.com/paper/2602.05078/) (2026)
- [Material Measurement Units for a Circular Economy: Foundations through a Review](https://mlsanity.com/paper/2103.01997/) (2021)
- [A Mobile Food Recognition System for Dietary Assessment](https://mlsanity.com/paper/2204.09432/) (2022)
- [No Mesh, No Problem: Estimating Coral Volume and Surface from Sparse Multi-View Images](https://mlsanity.com/paper/2509.11164/) (2025)

## Abstract (arXiv)

Due to the growing concern of chronic diseases and other health problems related to diet, there is a need to develop accurate methods to estimate an individual's food and energy intake. Measuring accurate dietary intake is an open research problem. In particular, accurate food portion estimation is challenging since the process of food preparation and consumption impose large variations on food shapes and appearances. In this paper, we present a food portion estimation method to estimate food energy (kilocalories) from food images using Generative Adversarial Networks (GAN). We introduce the concept of an "energy distribution" for each food image. To train the GAN, we design a food image dataset based on ground truth food labels and segmentation masks for each food image as well as energy information associated with the food image. Our goal is to learn the mapping of the food image to the food energy. We can then estimate food energy based on the energy distribution. We show that an average energy estimation error rate of 10.89% can be obtained by learning the image-to-energy mapping.