  Detecting an ingestion environment is an important aspect of monitoring
dietary intake. It provides insightful information for dietary assessment.
However, it is a challenging problem where human-based reviewing can be
tedious, and algorithm-based review suffers from data imbalance and perceptual
aliasing problems. To address these issues, we propose a neural network-based
method with a two-stage training framework that tactfully combines fine-tuning
and transfer learning techniques. Our method is evaluated on a newly collected
dataset called ``UA Free Living Study", which uses an egocentric wearable
camera, AIM-2 sensor, to simulate food consumption in free-living conditions.
The proposed training framework is applied to common neural network backbones,
combined with approaches in the general imbalanced classification field.
Experimental results on the collected dataset show that our proposed method for
automatic ingestion environment recognition successfully addresses the
challenging data imbalance problem in the dataset and achieves a promising
overall classification accuracy of 96.63%.
