Learning eating environments through scene clustering
This addresses the understudied relationship between eating environments and health for researchers and health agencies, though it appears incremental as a method adaptation.
The paper tackles the problem of automatically identifying eating environments from dietary study images using a novel image clustering method that extracts features at global and local scales with a deep neural network. Experimental results show it significantly outperforms existing clustering approaches.
It is well known that dietary habits have a significant influence on health. While many studies have been conducted to understand this relationship, little is known about the relationship between eating environments and health. Yet researchers and health agencies around the world have recognized the eating environment as a promising context for improving diet and health. In this paper, we propose an image clustering method to automatically extract the eating environments from eating occasion images captured during a community dwelling dietary study. Specifically, we are interested in learning how many different environments an individual consumes food in. Our method clusters images by extracting features at both global and local scales using a deep neural network. The variation in the number of clusters and images captured by different individual makes this a very challenging problem. Experimental results show that our method performs significantly better compared to several existing clustering approaches.