Calories Prediction from Food Images

Author:

Chokr Manal,Elbassuoni Shady

Abstract

Calculating the amount of calories in a given food item is now a common task. We propose a machine-learning-based approach to predict the amount of calories from food images. Our system does not require any input from the user, except from an image of the food item. We take a pragmatic approach to accurately predict the amount of calories in a food item and solve the problem in three phases. First, we identify the type of the food item in the image. Second, we estimate the size of the food item in grams. Finally, by taking into consideration the output of the first two phases, we predict the amount of calories in the photographed food item. All these three phases are based purely on supervised machinelearning. We show that this pipelined approach is very effective in predicting the amount of calories in a food item as compared to baseline approaches which directly predicts the amount of calories from the image.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Smartphone Based Food Classification: Applications, Challenges, and Future Prospects for Diabetics;2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET);2023-12-04

2. ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset;MultiMedia Modeling;2023

3. Enhancing Food Intake Tracking in Long-term Care With Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology: Validation and Feasibility Assessment;JMIR Aging;2022-11-17

4. Twin neural network regression;Applied AI Letters;2022-10-21

5. WiNE;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2022-09-06

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