Next Article in Journal
Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features
Previous Article in Journal
Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
Open AccessArticle

A New Approach to Image-Based Estimation of Food Volume

Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Parma, 43124 Parma, Italy
Author to whom correspondence should be addressed.
Academic Editor: Qianping Gu
Algorithms 2017, 10(2), 66;
Received: 19 April 2017 / Revised: 24 May 2017 / Accepted: 6 June 2017 / Published: 10 June 2017
PDF [8456 KB, uploaded 10 June 2017]


A balanced diet is the key to a healthy lifestyle and is crucial for preventing or dealing with many chronic diseases such as diabetes and obesity. Therefore, monitoring diet can be an effective way of improving people’s health. However, manual reporting of food intake has been shown to be inaccurate and often impractical. This paper presents a new approach to food intake quantity estimation using image-based modeling. The modeling method consists of three steps: firstly, a short video of the food is taken by the user’s smartphone. From such a video, six frames are selected based on the pictures’ viewpoints as determined by the smartphone’s orientation sensors. Secondly, the user marks one of the frames to seed an interactive segmentation algorithm. Segmentation is based on a Gaussian Mixture Model alongside the graph-cut algorithm. Finally, a customized image-based modeling algorithm generates a point-cloud to model the food. At the same time, a stochastic object-detection method locates a checkerboard used as size/ground reference. The modeling algorithm is optimized such that the use of six input images still results in an acceptable computation cost. In our evaluation procedure, we achieved an average accuracy of 92 % on a test set that includes images of different kinds of pasta and bread, with an average processing time of about 23 s. View Full-Text
Keywords: automatic diet monitoring; image analysis; interactive segmentation; image-based modeling; volume estimation automatic diet monitoring; image analysis; interactive segmentation; image-based modeling; volume estimation

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Hassannejad, H.; Matrella, G.; Ciampolini, P.; Munari, I.D.; Mordonini, M.; Cagnoni, S. A New Approach to Image-Based Estimation of Food Volume. Algorithms 2017, 10, 66.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top