# Personalized Classifier for Food Image Recognition

arXiv: https://arxiv.org/abs/1804.04600 · HTML: https://mlsanity.com/paper/1804.04600/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021)
- [Personalized Image Semantic Segmentation](https://mlsanity.com/paper/2107.13978/) (2021)
- [A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment](https://mlsanity.com/paper/2106.11776/) (2021)
- [Learning to Substitute Ingredients in Recipes](https://mlsanity.com/paper/2302.07960/) (2023)
- [Simulating Personal Food Consumption Patterns using a Modified Markov Chain](https://mlsanity.com/paper/2208.06709/) (2022)
- [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)
- [Calorie Aware Automatic Meal Kit Generation from an Image](https://mlsanity.com/paper/2112.09839/) (2021)
- [FoodLogAthl-218: Constructing a Real-World Food Image Dataset Using Dietary Management Applications](https://mlsanity.com/paper/2512.14574/) (2025)

## Abstract (arXiv)

Currently, food image recognition tasks are evaluated against fixed datasets. However, in real-world conditions, there are cases in which the number of samples in each class continues to increase and samples from novel classes appear. In particular, dynamic datasets in which each individual user creates samples and continues the updating process often have content that varies considerably between different users, and the number of samples per person is very limited. A single classifier common to all users cannot handle such dynamic data. Bridging the gap between the laboratory environment and the real world has not yet been accomplished on a large scale. Personalizing a classifier incrementally for each user is a promising way to do this. In this paper, we address the personalization problem, which involves adapting to the user's domain incrementally using a very limited number of samples. We propose a simple yet effective personalization framework which is a combination of the nearest class mean classifier and the 1-nearest neighbor classifier based on deep features. To conduct realistic experiments, we made use of a new dataset of daily food images collected by a food-logging application. Experimental results show that our proposed method significantly outperforms existing methods.