# Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy

arXiv: https://arxiv.org/abs/2211.07440 · HTML: https://mlsanity.com/paper/2211.07440/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 68 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network](https://mlsanity.com/paper/2008.05655/) (2020)
- [Visual Aware Hierarchy Based Food Recognition](https://mlsanity.com/paper/2012.03368/) (2020)
- [FoodX-251: A Dataset for Fine-grained Food Classification](https://mlsanity.com/paper/1907.06167/) (2019)
- [ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition](https://mlsanity.com/paper/1705.02743/) (2017)
- [FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging](https://mlsanity.com/paper/1909.11946/) (2019)
- [Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication](https://mlsanity.com/paper/2006.00790/) (2020)
- [Mining Discriminative Food Regions for Accurate Food Recognition](https://mlsanity.com/paper/2207.03692/) (2022)
- [Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants](https://mlsanity.com/paper/1711.05128/) (2017)
- [Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types](https://mlsanity.com/paper/1909.00134/) (2019)
- [Fusing CNNs and statistical indicators to improve image classification](https://mlsanity.com/paper/2012.11049/) (2020)
- [Food Recognition using Fusion of Classifiers based on CNNs](https://mlsanity.com/paper/1709.04864/) (2017)
- [Blockchain in the Internet of Things: Architectures and Implementation](https://mlsanity.com/paper/2004.05817/) (2020)
- [Towards Bottom-Up Analysis of Social Food](https://mlsanity.com/paper/1603.04497/) (2016)

## Built on by (most cited first, 4 of 4)

- [Are Vision-Language Models Ready for Dietary Assessment? Exploring the Next Frontier in AI-Powered Food Image Recognition](https://mlsanity.com/paper/2504.06925/) (2025)
- [Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence](https://mlsanity.com/paper/2409.08700/) (2024)
- [SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights](https://mlsanity.com/paper/2507.04412/) (2025)
- [Khana: A Comprehensive Indian Cuisine Dataset](https://mlsanity.com/paper/2509.06006/) (2025)

## Abstract (arXiv)

Maintaining a healthy lifestyle has become increasingly challenging in today's sedentary society marked by poor eating habits. To address this issue, both national and international organisations have made numerous efforts to promote healthier diets and increased physical activity. However, implementing these recommendations in daily life can be difficult, as they are often generic and not tailored to individuals. This study presents the AI4Food-NutritionDB database, the first nutrition database that incorporates food images and a nutrition taxonomy based on recommendations by national and international health authorities. The database offers a multi-level categorisation, comprising 6 nutritional levels, 19 main categories (e.g., "Meat"), 73 subcategories (e.g., "White Meat"), and 893 specific food products (e.g., "Chicken"). The AI4Food-NutritionDB opens the doors to new food computing approaches in terms of food intake frequency, quality, and categorisation. Also, we present a standardised experimental protocol and benchmark including three tasks based on the nutrition taxonomy (i.e., category, subcategory, and final product recognition). These resources are available to the research community, including our deep learning models trained on AI4Food-NutritionDB, which can serve as pre-trained models, achieving accurate recognition results for challenging food image databases.