Researches have shown that diet recording can help people increase awareness
of food intake and improve nutrition management, and thereby maintain a
healthier life. Recently, researchers have been working on smartphone-based
diet recording methods and applications that help users accomplish two tasks:
record what they eat and how much they eat. Although the former task has made
great progress through adopting image recognition technology, it is still a
challenge to estimate the volume of foods accurately and conveniently. In this
paper, we propose a novel method, named MUSEFood, for food volume estimation.
MUSEFood uses the camera to capture photos of the food, but unlike existing
volume measurement methods, MUSEFood requires neither training images with
volume information nor placing a reference object of known size while taking
photos. In addition, considering the impact of different containers on the
contour shape of foods, MUSEFood uses a multi-task learning framework to
improve the accuracy of food segmentation, and uses a differential model
applicable for various containers to further reduce the negative impact of
container differences on volume estimation accuracy. Furthermore, MUSEFood uses
the microphone and the speaker to accurately measure the vertical distance from
the camera to the food in a noisy environment, thus scaling the size of food in
the image to its actual size. The experiments on real foods indicate that
MUSEFood outperforms state-of-the-art approaches, and highly improves the speed
of food volume estimation.

本文提出了一种名为 MUSEFood 的新方法，利用智能手机的摄像头、麦克风和扬声器结合多任务学习框架，旨在解决估算食物容量准确度差的问题。实验证明，MUSEFood 可以高效地估算食物容量并具有比现有方法更好的性能。