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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
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2022
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2211.07440 |
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| _version_ | 1866913321333030912 |
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| author | Romero-Tapiador, Sergio Tolosana, Ruben Morales, Aythami Fierrez, Julian Vera-Rodriguez, Ruben Espinosa-Salinas, Isabel Freixer, Gala Pau, Enrique Carrillo de Santa de Molina, Ana Ramírez Ortega-Garcia, Javier |
| author_facet | Romero-Tapiador, Sergio Tolosana, Ruben Morales, Aythami Fierrez, Julian Vera-Rodriguez, Ruben Espinosa-Salinas, Isabel Freixer, Gala Pau, Enrique Carrillo de Santa de Molina, Ana Ramírez Ortega-Garcia, Javier |
| contents | Maintaining a healthy lifestyle has become increasingly challenging in today's sedentary society marked by poor eating habits. To address this issue, both national and international organisations have made numerous efforts to promote healthier diets and increased physical activity. However, implementing these recommendations in daily life can be difficult, as they are often generic and not tailored to individuals. This study presents the AI4Food-NutritionDB database, the first nutrition database that incorporates food images and a nutrition taxonomy based on recommendations by national and international health authorities. The database offers a multi-level categorisation, comprising 6 nutritional levels, 19 main categories (e.g., "Meat"), 73 subcategories (e.g., "White Meat"), and 893 specific food products (e.g., "Chicken"). The AI4Food-NutritionDB opens the doors to new food computing approaches in terms of food intake frequency, quality, and categorisation. Also, we present a standardised experimental protocol and benchmark including three tasks based on the nutrition taxonomy (i.e., category, subcategory, and final product recognition). These resources are available to the research community, including our deep learning models trained on AI4Food-NutritionDB, which can serve as pre-trained models, achieving accurate recognition results for challenging food image databases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_07440 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy Romero-Tapiador, Sergio Tolosana, Ruben Morales, Aythami Fierrez, Julian Vera-Rodriguez, Ruben Espinosa-Salinas, Isabel Freixer, Gala Pau, Enrique Carrillo de Santa de Molina, Ana Ramírez Ortega-Garcia, Javier Computer Vision and Pattern Recognition Multimedia Maintaining a healthy lifestyle has become increasingly challenging in today's sedentary society marked by poor eating habits. To address this issue, both national and international organisations have made numerous efforts to promote healthier diets and increased physical activity. However, implementing these recommendations in daily life can be difficult, as they are often generic and not tailored to individuals. This study presents the AI4Food-NutritionDB database, the first nutrition database that incorporates food images and a nutrition taxonomy based on recommendations by national and international health authorities. The database offers a multi-level categorisation, comprising 6 nutritional levels, 19 main categories (e.g., "Meat"), 73 subcategories (e.g., "White Meat"), and 893 specific food products (e.g., "Chicken"). The AI4Food-NutritionDB opens the doors to new food computing approaches in terms of food intake frequency, quality, and categorisation. Also, we present a standardised experimental protocol and benchmark including three tasks based on the nutrition taxonomy (i.e., category, subcategory, and final product recognition). These resources are available to the research community, including our deep learning models trained on AI4Food-NutritionDB, which can serve as pre-trained models, achieving accurate recognition results for challenging food image databases. |
| title | Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2211.07440 |