  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.
