A review on food recognition technology for health applications

  • Dario Allegra Department of Mathematics and Computer Science, University of Catania, Italy.
  • Sebastiano Battiato Department of Mathematics and Computer Science; Center of Excellence for the Acceleration of Harm Reduction (CoEHAR), University of Catania, Italy.
  • Alessandro Ortis | ortis@dmi.unict.it Department of Mathematics and Computer Science; Center of Excellence for the Acceleration of Harm Reduction (CoEHAR), University of Catania, Italy. http://orcid.org/0000-0003-3461-4679
  • Salvatore Urso Center of Excellence for the Acceleration of Harm Reduction (CoEHAR), University of Catania, Italy.
  • Riccardo Polosa Center of Excellence for the Acceleration of Harm Reduction (CoEHAR), University of Catania, Italy.

Abstract

Food understanding from digital media has become a challenge with important applications in many different domains. On the other hand, food is a crucial part of human life since the health is strictly affected by diet. The impact of food in people life led Computer Vision specialists to develop new methods for automatic food intake monitoring and food logging. In this review paper we provide an overview about automatic food intake monitoring, by focusing on technical aspects and Computer Vision works which solve the main involved tasks (i.e., classification, recognitions, segmentation, etc.). Specifically, we conducted a systematic review on main scientific databases, including interdisciplinary databases (i.e., Scopus) as well as academic databases in the field of computer science that focus on topics related to image understanding (i.e., recognition, analysis, retrieval). The search queries were based on the following key words: “food recognition”, “food classification”, “food portion estimation”, “food logging” and “food image dataset”. A total of 434 papers have been retrieved. We excluded 329 works in the first screening and performed a new check for the remaining 105 papers. Then, we manually added 5 recent relevant studies. Our final selection includes 23 papers that present systems for automatic food intake monitoring, as well as 46 papers which addressed Computer Vision tasks related food images analysis which we consider essential for a comprehensive overview about this research topic. A discussion that highlights the limitations of this research field is reported in conclusions.

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Published
2020-12-30
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Section
Reviews
Keywords:
food recognition, health technology, computer vision, food image classification, food image retrieval
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How to Cite
Allegra, D., Battiato, S., Ortis, A., Urso, S., & Polosa, R. (2020). A review on food recognition technology for health applications. Health Psychology Research, 8(3). https://doi.org/10.4081/hpr.2020.9297