Food Portion Estimation: From Pixels to Calories

TL;DR

This paper reviews various strategies for estimating food portions from images to improve dietary assessment accuracy, including deep learning and auxiliary input methods, addressing the challenge of 3D size estimation from 2D images.

Contribution

It provides a comprehensive overview of existing techniques for food portion estimation, highlighting recent advances and challenges in the field.

Findings

Deep learning improves portion size prediction accuracy.

Auxiliary inputs like depth maps enhance estimation precision.

Multiple strategies have been developed to address 3D size estimation from 2D images.

Abstract

Reliance on images for dietary assessment is an important strategy to accurately and conveniently monitor an individual's health, making it a vital mechanism in the prevention and care of chronic diseases and obesity. However, image-based dietary assessment suffers from estimating the three dimensional size of food from 2D image inputs. Many strategies have been devised to overcome this critical limitation such as the use of auxiliary inputs like depth maps, multi-view inputs, or model-based approaches such as template matching. Deep learning also helps bridge the gap by either using monocular images or combinations of the image and the auxillary inputs to precisely predict the output portion from the image input. In this paper, we explore the different strategies employed for accurate portion estimation.