Human Visual System vs Convolution Neural Networks in food recognition task: An empirical comparison

摘要

Automated food recognition from food plate is useful for smartphone-based applications promoting healthy lifestyles and for automated carbohydrate counting, e.g. targeted at type I diabetic patients, but the variation of appearance of food items makes it a difficult task. Convolution Neural Networks (CNNs) raised to prominence in recent years, and they will enable those applications if they are able to match HVS accuracy at least in meal classification. In this work we run an experimental comparison of accuracy between CNNs and HVS based on a simple meal recognition task. We set up a survey for humans with two phases, training and testing, and also give the food dataset to state-of-the-art CNNs. The results, considering some relevant variations in the setup, allow us to reach conclusions regarding the comparison, characteristics and limitations of CNNs, which are relevant for future improvements.