Goldilocks and the Three Parameters:Empirically Finding the 'Just Right' for Segmenting Food Images for the AFINI-T System

Measuring nutritional intake is a tool that is critical to themonitoring of health, both as an individual or of a group. It isespecially important in the monitoring of those at risk formalnutrition, an issue which costs billions of dollars globally, andcurrent methods used in practice are manual, time-consuming,and have inherent biases and inaccuracies. This study proposes anovel imaging system with a superpixel-based segmentationalgorithm as part of an automated nutritional intake system. Thestudy also examines three important parameters of the algorithmand their ideal values; region size and spatial regularization forsuperpixel segmentation, as well as spatial weighting inclustering. The experimental results demonstrate that theproposed system is effective in segmenting an image of a plate intoits constituent foods.