A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms
Abstract Managing daily nutrition is a prominent concern among individuals in contemporary society. The advancement of dietary assessment systems and applications utilizing images has facilitated the effective management of individuals' nutritional information and dietary habits over time. The...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-47885-0 |
| _version_ | 1827603717866651648 |
|---|---|
| author | Fotios S. Konstantakopoulos Eleni I. Georga Dimitrios I. Fotiadis |
| author_facet | Fotios S. Konstantakopoulos Eleni I. Georga Dimitrios I. Fotiadis |
| author_sort | Fotios S. Konstantakopoulos |
| collection | DOAJ |
| description | Abstract Managing daily nutrition is a prominent concern among individuals in contemporary society. The advancement of dietary assessment systems and applications utilizing images has facilitated the effective management of individuals' nutritional information and dietary habits over time. The determination of food weight or volume is a vital part in these systems for assessing food quantities and nutritional information. This study presents a novel methodology for evaluating the weight of food by utilizing extracted features from images and training them through advanced boosting regression algorithms. Α unique dataset of 23,052 annotated food images of Mediterranean cuisine, including 226 different dishes with a reference object placed next to the dish, was used to train the proposed pipeline. Then, using extracted features from the annotated images, such as food area, reference object area, food id, food category, and food weight, we built a dataframe with 24,996 records. Finally, we trained the weight estimation model by applying cross validation, hyperparameter tuning, and boosting regression algorithms such as XGBoost, CatBoost, and LightGBM. Between the predicted and actual weight values for each food in the proposed dataset, the proposed model achieves a mean weight absolute error 3.93 g, a mean absolute percentage error 3.73% and a root mean square error 6.05 g for the 226 food items of the Mediterranean Greek Food database (MedGRFood), setting new perspectives in food image-based weight and nutrition estimate models and systems. |
| first_indexed | 2024-03-09T05:44:39Z |
| format | Article |
| id | doaj.art-e46d97a9ef204b569bb3decd11c1a58e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T05:44:39Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e46d97a9ef204b569bb3decd11c1a58e2023-12-03T12:22:53ZengNature PortfolioScientific Reports2045-23222023-11-0113111410.1038/s41598-023-47885-0A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithmsFotios S. Konstantakopoulos0Eleni I. Georga1Dimitrios I. Fotiadis2Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of IoanninaUnit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of IoanninaUnit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of IoanninaAbstract Managing daily nutrition is a prominent concern among individuals in contemporary society. The advancement of dietary assessment systems and applications utilizing images has facilitated the effective management of individuals' nutritional information and dietary habits over time. The determination of food weight or volume is a vital part in these systems for assessing food quantities and nutritional information. This study presents a novel methodology for evaluating the weight of food by utilizing extracted features from images and training them through advanced boosting regression algorithms. Α unique dataset of 23,052 annotated food images of Mediterranean cuisine, including 226 different dishes with a reference object placed next to the dish, was used to train the proposed pipeline. Then, using extracted features from the annotated images, such as food area, reference object area, food id, food category, and food weight, we built a dataframe with 24,996 records. Finally, we trained the weight estimation model by applying cross validation, hyperparameter tuning, and boosting regression algorithms such as XGBoost, CatBoost, and LightGBM. Between the predicted and actual weight values for each food in the proposed dataset, the proposed model achieves a mean weight absolute error 3.93 g, a mean absolute percentage error 3.73% and a root mean square error 6.05 g for the 226 food items of the Mediterranean Greek Food database (MedGRFood), setting new perspectives in food image-based weight and nutrition estimate models and systems.https://doi.org/10.1038/s41598-023-47885-0 |
| spellingShingle | Fotios S. Konstantakopoulos Eleni I. Georga Dimitrios I. Fotiadis A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms Scientific Reports |
| title | A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms |
| title_full | A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms |
| title_fullStr | A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms |
| title_full_unstemmed | A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms |
| title_short | A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms |
| title_sort | novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms |
| url | https://doi.org/10.1038/s41598-023-47885-0 |
| work_keys_str_mv | AT fotiosskonstantakopoulos anovelapproachtoestimatetheweightoffooditemsbasedonfeaturesextractedfromanimageusingboostingalgorithms AT eleniigeorga anovelapproachtoestimatetheweightoffooditemsbasedonfeaturesextractedfromanimageusingboostingalgorithms AT dimitriosifotiadis anovelapproachtoestimatetheweightoffooditemsbasedonfeaturesextractedfromanimageusingboostingalgorithms AT fotiosskonstantakopoulos novelapproachtoestimatetheweightoffooditemsbasedonfeaturesextractedfromanimageusingboostingalgorithms AT eleniigeorga novelapproachtoestimatetheweightoffooditemsbasedonfeaturesextractedfromanimageusingboostingalgorithms AT dimitriosifotiadis novelapproachtoestimatetheweightoffooditemsbasedonfeaturesextractedfromanimageusingboostingalgorithms |