Personalized Food Image Classifier Considering Time-Dependent and Item-Dependent Food Distribution

Abstract

Since the development of food diaries could enable people to develop healthy eating habits, food image recognition is in high demand to reduce the effort in food recording. Previous studies have worked on this challenging domain with datasets having fixed numbers of samples and classes. However, in the real-world setting, it is impossible to include all of the foods in the database because the number of classes of foods is large and increases continually. In addition to that, inter-class similarity and intraclass diversity also bring difficulties to the recognition. In this paper, we solve these problems by using deep convolutional neural network features to build a personalized classifier which incrementally learns the user's data and adapts to the user's eating habit. As a result, we achieved the state-ofthe-art accuracy of food image recognition by the personalization of 300 food records per user.

Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

“…Research results related to the food industry mention deep learning methods (Convolutional Neural Network (CNN)-based food image recognition algorithm) used to derive food information (food type and portion size) from food image [45] or to propose an assistive calorie measurement system [46]. In [47] proposed a time-dependent food distribution model and a weight optimisation algorithm aimed at adapting the user's data to their eating habits. Deep learning has also been imposed in the waste sorting process to automate some of the waste handling tasks [48].…”