# A Mobile Food Recognition System for Dietary Assessment

arXiv: https://arxiv.org/abs/2204.09432 · HTML: https://mlsanity.com/paper/2204.09432/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes](https://mlsanity.com/paper/1809.00812/) (2018)
- [Inverse Cooking: Recipe Generation from Food Images](https://mlsanity.com/paper/1812.06164/) (2018)
- [ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network](https://mlsanity.com/paper/2008.05655/) (2020)
- [Online Continual Learning For Visual Food Classification](https://mlsanity.com/paper/2108.06781/) (2021)
- [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)
- [How to make a pizza: Learning a compositional layer-based GAN model](https://mlsanity.com/paper/1906.02839/) (2019)
- [Wide-Slice Residual Networks for Food Recognition](https://mlsanity.com/paper/1612.06543/) (2016)
- [Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks](https://mlsanity.com/paper/1802.09670/) (2018)
- [FoodNet: Recognizing Foods Using Ensemble of Deep Networks](https://mlsanity.com/paper/1709.09429/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Central Asian Food Dataset for Personalized Dietary Interventions, Extended Abstract](https://mlsanity.com/paper/2305.07257/) (2023)

## Abstract (arXiv)

Food recognition is an important task for a variety of applications, including managing health conditions and assisting visually impaired people. Several food recognition studies have focused on generic types of food or specific cuisines, however, food recognition with respect to Middle Eastern cuisines has remained unexplored. Therefore, in this paper we focus on developing a mobile friendly, Middle Eastern cuisine focused food recognition application for assisted living purposes. In order to enable a low-latency, high-accuracy food classification system, we opted to utilize the Mobilenet-v2 deep learning model. As some of the foods are more popular than the others, the number of samples per class in the used Middle Eastern food dataset is relatively imbalanced. To compensate for this problem, data augmentation methods are applied on the underrepresented classes. Experimental results show that using Mobilenet-v2 architecture for this task is beneficial in terms of both accuracy and the memory usage. With the model achieving 94% accuracy on 23 food classes, the developed mobile application has potential to serve the visually impaired in automatic food recognition via images.