Personalized Food Image Classification: Benchmark Datasets and New
  Baseline
Xinyue Pan Jiangpeng He Fengqing Zhu
Food image classification is a fundamental step of image-based dietary
assessment, enabling automated nutrient analysis from food images. Many current
methods employ deep neural networks to train on generic food image datasets
that do not reflect the dynamism of real-life food consumption patterns, in
which food images appear sequentially over time, reflecting the progression of
what an individual consumes. Personalized food classification aims to address
this problem by training a deep neural network using food images that reflect
the consumption pattern of each individual. However, this problem is
under-explored and there is a lack of benchmark datasets with individualized
food consumption patterns due to the difficulty in data collection. In this
work, we first introduce two benchmark personalized datasets including the
Food101-Personal, which is created based on surveys of daily dietary patterns
from participants in the real world, and the VFNPersonal, which is developed
based on a dietary study. In addition, we propose a new framework for
personalized food image classification by leveraging self-supervised learning
and temporal image feature information. Our method is evaluated on both
benchmark datasets and shows improved performance compared to existing works.
The dataset has been made available at:
https://skynet.ecn.purdue.edu/~pan161/dataset_personal.html