# A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

arXiv: https://arxiv.org/abs/2106.11776 · HTML: https://mlsanity.com/paper/2106.11776/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 176 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 26 of 26)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Measuring Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1708.02072/) (2017)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [Inverse Cooking: Recipe Generation from Food Images](https://mlsanity.com/paper/1812.06164/) (2018)
- [Fruit recognition from images using deep learning](https://mlsanity.com/paper/1712.00580/) (2017)
- [FoodX-251: A Dataset for Fine-grained Food Classification](https://mlsanity.com/paper/1907.06167/) (2019)
- [Multi-Task Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation](https://mlsanity.com/paper/2004.13188/) (2020)
- [DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment](https://mlsanity.com/paper/1606.05675/) (2016)
- [Two-view 3D Reconstruction for Food Volume Estimation](https://mlsanity.com/paper/1701.03330/) (2017)
- [Wide-Slice Residual Networks for Food Recognition](https://mlsanity.com/paper/1612.06543/) (2016)
- [Simultaneous Food Localization and Recognition](https://mlsanity.com/paper/1604.07953/) (2016)
- [Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants](https://mlsanity.com/paper/1711.05128/) (2017)
- [FoodNet: Recognizing Foods Using Ensemble of Deep Networks](https://mlsanity.com/paper/1709.09429/) (2017)
- [Personalized Classifier for Food Image Recognition](https://mlsanity.com/paper/1804.04600/) (2018)
- [Explaining Deep Neural Networks using Unsupervised Clustering](https://mlsanity.com/paper/2007.07477/) (2020)
- [Fine-grained Image Classification by Exploring Bipartite-Graph Labels](https://mlsanity.com/paper/1512.02665/) (2015)
- [Computer vision-based food calorie estimation: dataset, method, and experiment](https://mlsanity.com/paper/1705.07632/) (2017)
- [Food Ingredients Recognition through Multi-label Learning](https://mlsanity.com/paper/1707.08816/) (2017)
- [FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural Networks](https://mlsanity.com/paper/1909.05994/) (2019)
- [Fetishizing Food in Digital Age: #foodporn Around the World](https://mlsanity.com/paper/1603.00229/) (2016)
- [Towards Bottom-Up Analysis of Social Food](https://mlsanity.com/paper/1603.04497/) (2016)
- [Deep Convolutional Generative Adversarial Network Based Food Recognition Using Partially Labeled Data](https://mlsanity.com/paper/1812.10179/) (2018)

## Built on by (most cited first, 8 of 8)

- [Nutrition Estimation for Dietary Management: A Transformer Approach with Depth Sensing](https://mlsanity.com/paper/2406.01938/) (2024)
- [Food Image Classification and Segmentation with Attention-based Multiple Instance Learning](https://mlsanity.com/paper/2308.11452/) (2023)
- [NutrifyAI: An AI-Powered System for Real-Time Food Detection, Nutritional Analysis, and Personalized Meal Recommendations](https://mlsanity.com/paper/2408.10532/) (2024)
- [Shape-Preserving Generation of Food Images for Automatic Dietary Assessment](https://mlsanity.com/paper/2408.13358/) (2024)
- [Evaluating Gemini LLM in Food Image-Based Recipe and Nutrition Description with EfficientNet-B4 Visual Backbone](https://mlsanity.com/paper/2511.08215/) (2025)
- [Deep Learning-Based Meat Freshness Detection with Segmentation and OOD-Aware Classification](https://mlsanity.com/paper/2603.00368/) (2026)
- [DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images](https://mlsanity.com/paper/2604.06352/) (2026)
- [When Surveys Become Conversations: Adaptive Matrix Validation for AI-Assisted Interviews](https://mlsanity.com/paper/2606.24244/) (2026)

## Abstract (arXiv)

Dietary studies showed that dietary-related problem such as obesity is associated with other chronic diseases like hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods. This survey discusses the most performing methodologies that have been developed so far for automatic food recognition and volume estimation. First, we will present the rationale of visual-based methods for food recognition. The core of the paper is the presentation, discussion and evaluation of these methods on popular food image databases. Following that, we discussed the mobile applications that are implementing these methods. The survey ends with a discussion of research gaps and open issues in this area.