  Food image classification is essential for monitoring health and tracking
dietary in image-based dietary assessment methods. However, conventional
systems often rely on static datasets with fixed classes and uniform
distribution. In contrast, real-world food consumption patterns, shaped by
cultural, economic, and personal influences, involve dynamic and evolving data.
Thus, require the classification system to cope with continuously evolving
data. Online Class Incremental Learning (OCIL) addresses the challenge of
learning continuously from a single-pass data stream while adapting to the new
knowledge and reducing catastrophic forgetting. Experience Replay (ER) based
OCIL methods store a small portion of previous data and have shown encouraging
performance. However, most existing OCIL works assume that the distribution of
encountered data is perfectly balanced, which rarely happens in real-world
scenarios. In this work, we explore OCIL for real-world food image
classification by first introducing a probabilistic framework to simulate
realistic food consumption scenarios. Subsequently, we present an attachable
Dynamic Model Update (DMU) module designed for existing ER methods, which
enables the selection of relevant images for model training, addressing
challenges arising from data repetition and imbalanced sample occurrences
inherent in realistic food consumption patterns within the OCIL framework. Our
performance evaluation demonstrates significant enhancements compared to
established ER methods, showing great potential for lifelong learning in
real-world food image classification scenarios. The code of our method is
publicly accessible at
https://gitlab.com/viper-purdue/OCIL-real-world-food-image-classification
