# An Integrated System for Mobile Image-Based Dietary Assessment

arXiv: https://arxiv.org/abs/2110.01754 · HTML: https://mlsanity.com/paper/2110.01754/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Incremental Learning In Online Scenario](https://mlsanity.com/paper/2003.13191/) (2020)
- [Online Continual Learning For Visual Food Classification](https://mlsanity.com/paper/2108.06781/) (2021)
- [Visual Aware Hierarchy Based Food Recognition](https://mlsanity.com/paper/2012.03368/) (2020)
- [Multi-Task Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation](https://mlsanity.com/paper/2004.13188/) (2020)
- [An End-to-End Food Image Analysis System](https://mlsanity.com/paper/2102.00645/) (2021)
- [Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks](https://mlsanity.com/paper/1802.09670/) (2018)
- [Saliency-Aware Class-Agnostic Food Image Segmentation](https://mlsanity.com/paper/2102.06882/) (2021)
- [Semi-Automatic Crowdsourcing Tool for Online Food Image Collection and Annotation](https://mlsanity.com/paper/1910.05242/) (2019)
- [Learning eating environments through scene clustering](https://mlsanity.com/paper/1910.11367/) (2019)

## Built on by (most cited first, 19 of 19)

- [Long-Tailed Continual Learning For Visual Food Recognition](https://mlsanity.com/paper/2307.00183/) (2023)
- [Long-tailed Food Classification](https://mlsanity.com/paper/2210.14748/) (2022)
- [Food Portion Estimation via 3D Object Scaling](https://mlsanity.com/paper/2404.12257/) (2024)
- [MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results](https://mlsanity.com/paper/2407.09285/) (2024)
- [Simulating Personal Food Consumption Patterns using a Modified Markov Chain](https://mlsanity.com/paper/2208.06709/) (2022)
- [An Improved Encoder-Decoder Framework for Food Energy Estimation](https://mlsanity.com/paper/2309.00468/) (2023)
- [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)
- [Muti-Stage Hierarchical Food Classification](https://mlsanity.com/paper/2309.01075/) (2023)
- [Personalized Food Image Classification: Benchmark Datasets and New Baseline](https://mlsanity.com/paper/2309.08744/) (2023)
- [Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor](https://mlsanity.com/paper/2405.07827/) (2024)
- [Conditional Synthetic Food Image Generation](https://mlsanity.com/paper/2303.09005/) (2023)
- [Comprehensive Evaluation of Large Multimodal Models for Nutrition Analysis: A New Benchmark Enriched with Contextual Metadata](https://mlsanity.com/paper/2507.07048/) (2025)
- [Self-Supervised Visual Representation Learning on Food Images](https://mlsanity.com/paper/2303.09046/) (2023)
- [How Much You Ate? Food Portion Estimation on Spoons](https://mlsanity.com/paper/2405.08717/) (2024)
- [FMiFood: Multi-modal Contrastive Learning for Food Image Classification](https://mlsanity.com/paper/2408.03922/) (2024)
- [VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimation](https://mlsanity.com/paper/2505.10205/) (2025)
- [Training-Free Text-to-Image Compositional Food Generation via Prompt Grafting](https://mlsanity.com/paper/2601.17666/) (2026)
- [When Surveys Become Conversations: Adaptive Matrix Validation for AI-Assisted Interviews](https://mlsanity.com/paper/2606.24244/) (2026)

## Abstract (arXiv)

Accurate assessment of dietary intake requires improved tools to overcome limitations of current methods including user burden and measurement error. Emerging technologies such as image-based approaches using advanced machine learning techniques coupled with widely available mobile devices present new opportunities to improve the accuracy of dietary assessment that is cost-effective, convenient and timely. However, the quality and quantity of datasets are essential for achieving good performance for automated image analysis. Building a large image dataset with high quality groundtruth annotation is a challenging problem, especially for food images as the associated nutrition information needs to be provided or verified by trained dietitians with domain knowledge. In this paper, we present the design and development of a mobile, image-based dietary assessment system to capture and analyze dietary intake, which has been deployed in both controlled-feeding and community-dwelling dietary studies. Our system is capable of collecting high quality food images in naturalistic settings and provides groundtruth annotations for developing new computational approaches.