Food object recognition using a mobile device: Evaluation of currently implemented systems

Food object recognition systems present an attractive and useful research field since they enable objective measurements of eating activity. This feature is helpful and welcome in many dieting related instances, especially for managing health conditions or for analyzing eating patterns of research s...

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Bibliographic Details
Published in:Trends in food science & technology 2020-05, Vol.99, p.460-471
Main Authors: Knez, Simon, Šajn, Luka
Format: Article
Language:English
Subjects:
Computer vision
Eating
Electronic devices
Evaluation
Feature extraction
Food
Food processing
Food recognition
Image acquisition
Image classification
Image processing
Image segmentation
Mobile application
Nutrition
Object recognition
Pattern recognition
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Summary:Food object recognition systems present an attractive and useful research field since they enable objective measurements of eating activity. This feature is helpful and welcome in many dieting related instances, especially for managing health conditions or for analyzing eating patterns of research subjects. We evaluate current food object recognition systems that were implemented on a mobile device. The evaluation was provided by analysing each particular system through its food recognition process. The whole recognition process was divided into 6 distinct stages: image acquisition, image processing, image segmentation, feature extraction, image classification, and volume estimation. Through the analysis, the authors provide a categorization of mobile food recognition systems: recorder systems, suggester systems, and clinical responders. Each group is aimed at a different scenario which helps to identify features a particular system should focus its development on. •11 (mobile) food object recognition systems are described (from 2013 to 2018).•Each system is described individually through 6 stages of object recognition.•Establishing 3 categories of mobile food object recognition systems.•Providing guidelines and possible improvements for systems belonging to each category.
ISSN:0924-2244
1879-3053
DOI:10.1016/j.tifs.2020.03.017