  Food image classification serves as the foundation of image-based dietary
assessment to predict food categories. Since there are many different food
classes in real life, conventional models cannot achieve sufficiently high
accuracy. Personalized classifiers aim to largely improve the accuracy of food
image classification for each individual. However, a lack of public personal
food consumption data proves to be a challenge for training such models. To
address this issue, we propose a novel framework to simulate personal food
consumption data patterns, leveraging the use of a modified Markov chain model
and self-supervised learning. Our method is capable of creating an accurate
future data pattern from a limited amount of initial data, and our simulated
data patterns can be closely correlated with the initial data pattern.
Furthermore, we use Dynamic Time Warping distance and Kullback-Leibler
divergence as metrics to evaluate the effectiveness of our method on the public
Food-101 dataset. Our experimental results demonstrate promising performance
compared with random simulation and the original Markov chain method.
