论文标题
使用改良的马尔可夫链模拟个人食品消费模式
Simulating Personal Food Consumption Patterns using a Modified Markov Chain
论文作者
论文摘要
食品图像分类是基于图像的饮食评估以预测食物类别的基础。由于现实生活中有许多不同的食物类别，因此传统模型无法达到足够高的准确性。个性化分类器旨在在很大程度上提高每个人的食物图像分类的准确性。但是，缺乏公共个人食品消费数据被证明是培训此类模型的挑战。为了解决这个问题，我们提出了一个新颖的框架，以模拟个人食品消耗数据模式，利用修改后的马尔可夫链模型和自我监督的学习。我们的方法能够从有限的初始数据中创建准确的未来数据模式，并且我们的模拟数据模式可以与初始数据模式密切相关。此外，我们使用动态的时间翘曲距离和Kullback-Leibler Divergence作为指标来评估我们方法对公共食品-101数据集的有效性。我们的实验结果表明，与随机模拟和原始马尔可夫链方法相比，表现出色。
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.
