This paper is in the following e-collection/theme issue:

Food Sciences and Nutrition Engineering (58) Crowdsourcing and Mechanical Turks (103) Obesity and Nutrition as Public Health Problem (396)

Published on in Vol 7, No 2 (2018): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9359, first published .
Calorie Estimation From Pictures of Food: Crowdsourcing Study

Calorie Estimation From Pictures of Food: Crowdsourcing Study

Calorie Estimation From Pictures of Food: Crowdsourcing Study

Authors of this article:

Jun Zhou1 Author Orcid Image ;   Dane Bell2 Author Orcid Image ;   Sabrina Nusrat3 Author Orcid Image ;   Melanie Hingle4 Author Orcid Image ;   Mihai Surdeanu3 Author Orcid Image ;   Stephen Kobourov3 Author Orcid Image
Article Authors Cited by (13) Tweetations (7) Metrics

Journals

  1. Zhou L, Parmanto B. Development and Validation of a Comprehensive Well-Being Scale for People in the University Environment (Pitt Wellness Scale) Using a Crowdsourcing Approach: Cross-Sectional Study. Journal of Medical Internet Research 2020;22(4):e15075 View
  2. Hahn Y, Oh B. Visualization of Actionable Food Knowledge: Lessons from the Development of the Food Balance Lunchbox. Archives of Design Research 2020;33(1):37 View
  3. Ventura V, Cavaliere A, Iannò B. #Socialfood: Virtuous or vicious? A systematic review. Trends in Food Science & Technology 2021;110:674 View
  4. Dlamini S, Mukoma G, Norris S. Should fast-food nutritional labelling in South Africa be mandatory?. South African Journal of Clinical Nutrition 2022;35(4):155 View
  5. Smith S, Adam M, Manning G, Burrows T, Collins C, Rollo M. Food Volume Estimation by Integrating 3D Image Projection and Manual Wire Mesh Transformations. IEEE Access 2022;10:48367 View
  6. Leino A, Magee J, Kershaw D, Pai M, Park J. A Comprehensive Mixed‐Method Approach to Characterize the Source of Diurnal Tacrolimus Exposure Variability in Children: Systematic Review, Meta‐analysis, and Application to an Existing Data Set. The Journal of Clinical Pharmacology 2024;64(3):334 View
  7. Durma A, Sosnowska M, Durma A, Śmiałowski A, Czupryniak L. Analysis of Food Perception in Slim, Overweight, or Obese Individuals. Nutrients 2025;17(13):2054 View
  8. Gemesi K, Holzmann S, Böhm M, Leipold N, Hauptmann H, Lurz M, Groh G, Hauner H, Krcmar H, Holzapfel C. ‘The wisdom of crowds’: a survey on the rating of nutritional values of meals in digital pictures. BMJ Nutrition, Prevention & Health 2025;8(1):184 View
  9. Dugas K, Giroux M, Guerroudj A, Leger J, Rouhafzay A, Rouhafzay G, Jbilou J. Calorie Counting Apps for Monitoring and Managing Calorie Intake in Adults living with Weight-Related Chronic Diseases: A Decade-long Scoping Review (2013-2024) (Preprint). JMIR mHealth and uHealth 2024 View

Books/Policy Documents

  1. Florance Mary M, Hemakumar B. Evolutionary Computing and Mobile Sustainable Networks. View
  2. Sarkar S, Islam K, Partho D, Rashed M, Das D, Deka V. Data Science and Communication Engineering. View

Conference Proceedings

  1. Ruede R, Heusser V, Frank L, Roitberg A, Haurilet M, Stiefelhagen R. 2020 25th International Conference on Pattern Recognition (ICPR). Multi-Task Learning for Calorie Prediction on a Novel Large-Scale Recipe Dataset Enriched with Nutritional Information View
  2. Yarde P, Bordoloi D, Chavan R, Vekariya V, Patil H, Natrayan L. 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). A Deep Learning Neural Network-based System for Food Recognition and Calorie Estimation View