{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T02:26:16Z","timestamp":1781922376278,"version":"3.54.5"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T00:00:00Z","timestamp":1713398400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T00:00:00Z","timestamp":1713398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100012818","name":"Comunidad de Madrid","doi-asserted-by":"publisher","award":["AI4FOOD-CM (Y2020\/TCS6654)"],"award-info":[{"award-number":["AI4FOOD-CM (Y2020\/TCS6654)"]}],"id":[{"id":"10.13039\/100012818","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012818","name":"Comunidad de Madrid","doi-asserted-by":"publisher","award":["FACINGLCOVID-CM (PD2022-004-REACT-EU)"],"award-info":[{"award-number":["FACINGLCOVID-CM (PD2022-004-REACT-EU)"]}],"id":[{"id":"10.13039\/100012818","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["HumanCAIC (TED2021-131787BI00 MICINN)"],"award-info":[{"award-number":["HumanCAIC (TED2021-131787BI00 MICINN)"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Maintaining a healthy lifestyle has become increasingly challenging in today\u2019s 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., \u201cMeat\u201d), 73 subcategories (e.g., \u201cWhite Meat\u201d), and 893 specific food products (e.g., \u201cChicken\u201d). 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. All these resources are available in GitHub (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/BiDAlab\/AI4Food-NutritionDB\" ext-link-type=\"uri\">https:\/\/github.com\/BiDAlab\/AI4Food-NutritionDB<\/jats:ext-link>).<\/jats:p>","DOI":"10.1007\/s11042-024-19161-4","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T06:01:56Z","timestamp":1713420116000},"page":"1945-1966","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Leveraging automatic personalised nutrition: food image recognition benchmark and dataset based on nutrition taxonomy"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8919-8687","authenticated-orcid":false,"given":"Sergio","family":"Romero-Tapiador","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9393-3066","authenticated-orcid":false,"given":"Ruben","family":"Tolosana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7268-4785","authenticated-orcid":false,"given":"Aythami","family":"Morales","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6343-5656","authenticated-orcid":false,"given":"Julian","family":"Fierrez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6338-8511","authenticated-orcid":false,"given":"Ruben","family":"Vera-Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9526-891X","authenticated-orcid":false,"given":"Isabel","family":"Espinosa-Salinas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8182-6784","authenticated-orcid":false,"given":"Gala","family":"Freixer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2310-2267","authenticated-orcid":false,"given":"Enrique","family":"Carrillo de Santa Pau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1439-7494","authenticated-orcid":false,"given":"Ana","family":"Ram\u00edrez de Molina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0557-1948","authenticated-orcid":false,"given":"Javier","family":"Ortega-Garcia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,18]]},"reference":[{"key":"19161_CR1","doi-asserted-by":"publisher","unstructured":"Acharya B, Ghosh A, Panda S et al (2023) Automated Plant Recognition System with Geographical Position Selection for Medicinal Plants. Adv Multimed 2023. https:\/\/doi.org\/10.1155\/2023\/3974346","DOI":"10.1155\/2023\/3974346"},{"key":"19161_CR2","doi-asserted-by":"publisher","unstructured":"Acien A, Morales A, Vera-Rodriguez R, et\u00a0al (2020) Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication. In: Proc. IEEE conference on computers, software, and applications, pp 1273\u20131278. https:\/\/doi.org\/10.1109\/COMPSAC48688.2020.00-81","DOI":"10.1109\/COMPSAC48688.2020.00-81"},{"key":"19161_CR3","doi-asserted-by":"publisher","unstructured":"Aguilar E, Bola\u00f1os M, Radeva P (2017) Food Recognition using Fusion of Classifiers Based on CNNs. In: Proc. international conference on image analysis and processing, Springer, pp 213\u2013224. https:\/\/doi.org\/10.1007\/978-3-319-68548-9_20","DOI":"10.1007\/978-3-319-68548-9_20"},{"issue":"12","key":"19161_CR4","doi-asserted-by":"publisher","first-page":"3266","DOI":"10.1109\/TMM.2018.2831627","volume":"20","author":"E Aguilar","year":"2018","unstructured":"Aguilar E, Remeseiro B, Bola\u00f1os M et al (2018) Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants. IEEE Trans Multimed 20(12):3266\u20133275. https:\/\/doi.org\/10.1109\/TMM.2018.2831627","journal-title":"IEEE Trans Multimed"},{"key":"19161_CR5","doi-asserted-by":"publisher","first-page":"9177","DOI":"10.1007\/s11042-020-10099-x","volume":"80","author":"S Badshah","year":"2021","unstructured":"Badshah S, Khan AA, Hussain S et al (2021) What Users Really Think about the Usability of Smartphone Applications: Diversity based Empirical Investigation. Multimed Tools Appl 80:9177\u20139207. https:\/\/doi.org\/10.1007\/s11042-020-10099-x","journal-title":"Multimed Tools Appl"},{"key":"19161_CR6","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A Barredo Arrieta","year":"2020","unstructured":"Barredo Arrieta A, D\u00edaz-Rodr\u00edguez N, Del Ser J et al (2020) Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Inf Fusion 58:82\u2013115. https:\/\/doi.org\/10.1016\/j.inffus.2019.12.012","journal-title":"Inf Fusion"},{"key":"19161_CR7","doi-asserted-by":"publisher","first-page":"307","DOI":"10.3389\/fpsyg.2019.00307","volume":"10","author":"J Blechert","year":"2019","unstructured":"Blechert J, Lender A, Polk S et al (2019) Food-Pics_Extended - An Image Database for Experimental Research on Eating and Appetite: Additional Images, Normative Ratings and an Updated Review. Front Psychol 10:307. https:\/\/doi.org\/10.3389\/fpsyg.2019.00307","journal-title":"Front Psychol"},{"key":"19161_CR8","doi-asserted-by":"publisher","unstructured":"Bossard L, Guillaumin M, Van\u00a0Gool L (2014) Food-101 \u2013 Mining Discriminative Components with Random Forests. In: Fleet D, Pajdla T, Schiele B et\u00a0al (eds) Proc. European Conference on Computer Vision, pp 446\u2013461. https:\/\/doi.org\/10.1007\/978-3-319-10599-4_29","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"19161_CR9","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.appet.2015.08.041","volume":"96","author":"L Charbonnier","year":"2016","unstructured":"Charbonnier L, van Meer F, van der Laan LN et al (2016) Standardized Food Images: A Photographing Protocol and Image Database. Appetite 96:166\u2013173. https:\/\/doi.org\/10.1016\/j.appet.2015.08.041","journal-title":"Appetite"},{"key":"19161_CR10","doi-asserted-by":"publisher","unstructured":"Chen J, Ngo CW (2016) Deep-based Ingredient Recognition for Cooking Recipe Retrival. ACM Multimedia pp 32\u201341. https:\/\/doi.org\/10.1145\/2964284.2964315","DOI":"10.1145\/2964284.2964315"},{"key":"19161_CR11","doi-asserted-by":"publisher","first-page":"1514","DOI":"10.1109\/TIP.2020.3045639","volume":"30","author":"J Chen","year":"2021","unstructured":"Chen J, Zhu B, Ngo CW et al (2021) A Study of Multi-Task and Region-Wise Deep Learning for Food Ingredient Recognition. IEEE Trans Image Process 30:1514\u20131526. https:\/\/doi.org\/10.1109\/TIP.2020.3045639","journal-title":"IEEE Trans Image Process"},{"issue":"22","key":"19161_CR12","doi-asserted-by":"publisher","first-page":"4712","DOI":"10.3390\/rs13224712","volume":"13","author":"L Chen","year":"2021","unstructured":"Chen L, Li S, Bai Q et al (2021) Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sens 13(22):4712. https:\/\/doi.org\/10.3390\/rs13224712","journal-title":"Remote Sens"},{"key":"19161_CR13","doi-asserted-by":"publisher","unstructured":"Chen M, Dhingra K, Wu W et\u00a0al (2009) PFID: Pittsburgh Fast-Food Image Dataset. In: Proc. IEEE International Conference on Image Processing, pp 289\u2013292. https:\/\/doi.org\/10.1109\/ICIP.2009.5413511","DOI":"10.1109\/ICIP.2009.5413511"},{"key":"19161_CR14","doi-asserted-by":"publisher","unstructured":"Chen X, Zhou H, Zhu Y et\u00a0al (2017) ChineseFoodNet: A Large-Scale Image Dataset for Chinese Food Recognition. arXiv:1705.02743. https:\/\/doi.org\/10.48550\/arXiv.1705.02743","DOI":"10.48550\/arXiv.1705.02743"},{"key":"19161_CR15","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: Deep Learning with Depthwise Separable Convolutions. In: Proc. Conference on Computer Vision and Pattern Recognition, pp 1251\u20131258. https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"19161_CR16","doi-asserted-by":"publisher","unstructured":"Ciocca G, Napoletano P, Schettini R (2015) Food Recognition and Leftover Estimation for Daily Diet Monitoring. In: Proc. International Conference on Image Analysis and Processing, pp 334\u2013341. https:\/\/doi.org\/10.1007\/978-3-319-23222-5_41","DOI":"10.1007\/978-3-319-23222-5_41"},{"issue":"3","key":"19161_CR17","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1109\/JBHI.2016.2636441","volume":"21","author":"G Ciocca","year":"2016","unstructured":"Ciocca G, Napoletano P, Schettini R (2016) Food Recognition: A New Dataset, Experiments, and Results. IEEE J Biomed Health Informa 21(3):588\u2013598. https:\/\/doi.org\/10.1109\/JBHI.2016.2636441","journal-title":"IEEE J Biomed Health Informa"},{"key":"19161_CR18","doi-asserted-by":"publisher","unstructured":"Ciocca G, Napoletano P, Schettini R (2017) Learning CNN-based Features for Retrieval of Food Images. In: Battiato S, Farinella GM, Leo M et\u00a0al (eds) Proc. New Trends in Image Analysis and Processing, pp 426\u2013434. https:\/\/doi.org\/10.1007\/978-3-319-70742-6_41","DOI":"10.1007\/978-3-319-70742-6_41"},{"key":"19161_CR19","doi-asserted-by":"publisher","unstructured":"Deandres-Tame I, Tolosana R, Vera-Rodriguez R et\u00a0al (2024) How Good is ChatGPT at Face Biometrics? A First Look into Recognition, Soft Biometrics, and Explainability. IEEE Access pp 1\u20131. https:\/\/doi.org\/10.1109\/ACCESS.2024.3370437","DOI":"10.1109\/ACCESS.2024.3370437"},{"key":"19161_CR20","doi-asserted-by":"publisher","unstructured":"Delgado-Mohatar O, Tolosana R, Fierrez J et\u00a0al (2020) Blockchain in the Internet of Things: Architectures and Implementation. In: Proc. IEEE conference on computers, software, and applications, pp 1072\u20131077. https:\/\/doi.org\/10.1109\/COMPSAC48688.2020.0-131","DOI":"10.1109\/COMPSAC48688.2020.0-131"},{"key":"19161_CR21","doi-asserted-by":"publisher","unstructured":"Deng L, Chen J, Sun Q et\u00a0al (2019) Mixed-Dish Recognition with Contextual Relation Networks. In: Proc. ACM international conference on multimedia, pp 112\u2013120. https:\/\/doi.org\/10.1145\/3343031.3351147","DOI":"10.1145\/3343031.3351147"},{"key":"19161_CR22","doi-asserted-by":"publisher","unstructured":"Dooley DM, Griffiths EJ, Gosal GS et\u00a0al (2018) FoodOn: A Harmonized Food Ontology to Increase Global Food Traceability, Quality Control and Data Integration. npj Science of Food 2(1):1\u201310. https:\/\/doi.org\/10.1038\/s41538-018-0032-6","DOI":"10.1038\/s41538-018-0032-6"},{"key":"19161_CR23","doi-asserted-by":"publisher","unstructured":"Farinella GM, Allegra D, Stanco F (2014) A Benchmark Dataset to Study the Representation of Food Images. In: Proc. European conference on computer vision, Springer, pp 584\u2013599. https:\/\/doi.org\/10.1007\/978-3-319-16199-0_41","DOI":"10.1007\/978-3-319-16199-0_41"},{"key":"19161_CR24","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.compbiomed.2016.07.006","volume":"77","author":"GM Farinella","year":"2016","unstructured":"Farinella GM, Allegra D, Moltisanti M et al (2016) Retrieval and Classification of Food Images. Comput Biol Med 77:23\u201339. https:\/\/doi.org\/10.1016\/j.compbiomed.2016.07.006","journal-title":"Comput Biol Med"},{"issue":"16","key":"19161_CR25","doi-asserted-by":"publisher","first-page":"2628","DOI":"10.1016\/j.patrec.2005.06.008","volume":"26","author":"J Fierrez-Aguilar","year":"2005","unstructured":"Fierrez-Aguilar J, Garcia-Romero D, Ortega-Garcia J et al (2005) Adapted User-Dependent Multimodal Biometric Authentication Exploiting General Information. Pattern Recogn Lett 26(16):2628\u20132639. https:\/\/doi.org\/10.1016\/j.patrec.2005.06.008","journal-title":"Pattern Recogn Lett"},{"issue":"6","key":"19161_CR26","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.amepre.2011.10.026","volume":"42","author":"EA Finkelstein","year":"2012","unstructured":"Finkelstein EA, Khavjou OA, Thompson H et al (2012) Obesity and Severe Obesity Forecasts through 2030. Am J Prev Med 42(6):563\u2013570. https:\/\/doi.org\/10.1016\/j.amepre.2011.10.026","journal-title":"Am J Prev Med"},{"key":"19161_CR27","doi-asserted-by":"publisher","unstructured":"Fontana JM, Farooq M, Sazonov E (2021) Detection and Characterization of Food Intake by Wearable Sensors. In: Wearable Sensors, pp 541\u2013574. https:\/\/doi.org\/10.1016\/B978-0-12-819246-7.00020-6","DOI":"10.1016\/B978-0-12-819246-7.00020-6"},{"key":"19161_CR28","doi-asserted-by":"publisher","first-page":"2610","DOI":"10.1016\/j.patcog.2011.12.011","volume":"45","author":"J Galbally","year":"2012","unstructured":"Galbally J, Plamondon R, Fierrez J et al (2012) Synthetic On-line Signature Generation. Part I: Methodology and Algorithms. Pattern Recognition 45:2610\u20132621. https:\/\/doi.org\/10.1016\/j.patcog.2011.12.011","journal-title":"Pattern Recognition"},{"key":"19161_CR29","doi-asserted-by":"publisher","unstructured":"G\u00fcng\u00f6r C, Baltac\u0131 F, Erdem A et\u00a0al (2017) Turkish Cuisine: A Benchmark Dataset with Turkish Meals for Food Recognition. In: Proc. Signal Processing and Communications Applications Conference, pp 1\u20134. https:\/\/doi.org\/10.1109\/SIU.2017.7960494","DOI":"10.1109\/SIU.2017.7960494"},{"key":"19161_CR30","doi-asserted-by":"publisher","unstructured":"Hou S, Feng Y, Wang Z (2017) VegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization. In: Proc. IEEE international conference on computer vision, pp 541\u2013549. https:\/\/doi.org\/10.1109\/ICCV.2017.66","DOI":"10.1109\/ICCV.2017.66"},{"key":"19161_CR31","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-Excitation Networks. In: Proc. Conference on Computer Vision and Pattern Recognition, pp 7132\u20137141. https:\/\/doi.org\/10.1109\/CVPR.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"19161_CR32","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.inffus.2021.09.012","volume":"79","author":"J Huertas-Tato","year":"2022","unstructured":"Huertas-Tato J, Martin A, Fierrez J et al (2022) Fusing CNNs and Statistical Indicators to Improve Image Classification. Inf Fusion 79:174\u2013187. https:\/\/doi.org\/10.1016\/j.inffus.2021.09.012","journal-title":"Inf Fusion"},{"key":"19161_CR33","doi-asserted-by":"publisher","unstructured":"Jalal M, Wang K, Jefferson S et\u00a0al (2019) Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types. In: Proc. international workshop on multimedia assisted dietary management, pp 50\u201359. https:\/\/doi.org\/10.1145\/3347448.3357170","DOI":"10.1145\/3347448.3357170"},{"key":"19161_CR34","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1109\/TIP.2019.2929447","volume":"29","author":"S Jiang","year":"2020","unstructured":"Jiang S, Min W, Liu L et al (2020) Multi-Scale Multi-View Deep Feature Aggregation for Food Recognition. IEEE Trans Image Process 29:265\u2013276. https:\/\/doi.org\/10.1109\/TIP.2019.2929447","journal-title":"IEEE Trans Image Process"},{"key":"19161_CR35","doi-asserted-by":"publisher","unstructured":"Joutou T, Yanai K (2009) A Food Image Recognition System with Multiple Kernel Learning. In: Proc. IEEE international conference on image processing, pp 285\u2013288. https:\/\/doi.org\/10.1109\/ICIP.2009.5413400","DOI":"10.1109\/ICIP.2009.5413400"},{"key":"19161_CR36","doi-asserted-by":"publisher","unstructured":"Kaur P, Sikka K, Wang W et\u00a0al (2019) Foodx-251: A Dataset for Fine-Grained Food Classification. arXiv:1907.06167. https:\/\/doi.org\/10.48550\/arXiv.1907.06167","DOI":"10.48550\/arXiv.1907.06167"},{"key":"19161_CR37","doi-asserted-by":"publisher","unstructured":"Kawano Y, Yanai K (2014) Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation. In: Proc. ECCV workshop on transferring and adapting source knowledge in computer vision, pp 3\u201317. https:\/\/doi.org\/10.1007\/978-3-319-16199-0_1","DOI":"10.1007\/978-3-319-16199-0_1"},{"key":"19161_CR38","doi-asserted-by":"publisher","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet Classification with Deep Convolutional Neural Networks. Adv Neural Inf Process Syst 25. https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"19161_CR39","doi-asserted-by":"publisher","unstructured":"Mao R, He J, Shao Z et\u00a0al (2021) Visual Aware Hierarchy Based Food Recognition. In: Proc. International Conference on Pattern Recognition, pp 571\u2013598. https:\/\/doi.org\/10.1007\/978-3-030-68821-9_47","DOI":"10.1007\/978-3-030-68821-9_47"},{"key":"19161_CR40","doi-asserted-by":"publisher","unstructured":"Marcus J (2014) Nutrition Basics: What is Inside Food, How it Functions and Healthy Guidelines. Culinary Nutrition pp 1\u201350. https:\/\/doi.org\/10.1016\/B978-0-12-391882-6.00001-7","DOI":"10.1016\/B978-0-12-391882-6.00001-7"},{"key":"19161_CR41","doi-asserted-by":"publisher","unstructured":"Matsuda Y, Hoashi H, Yanai K (2012) Recognition of Multiple-Food Images by Detecting Candidate Regions. In: Proc. IEEE International Conference on Multimedia and Expo, pp 25\u201330. https:\/\/doi.org\/10.1109\/ICME.2012.157","DOI":"10.1109\/ICME.2012.157"},{"key":"19161_CR42","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.compbiomed.2018.02.008","volume":"95","author":"P McAllister","year":"2018","unstructured":"McAllister P, Zheng H, Bond R et al (2018) Combining Deep Residual Neural Network Features with Supervised Machine Learning Algorithms to Classify Diverse Food Image Datasets. Comput Biol Med 95:217\u2013233. https:\/\/doi.org\/10.1016\/j.compbiomed.2018.02.008","journal-title":"Comput Biol Med"},{"key":"19161_CR43","doi-asserted-by":"publisher","unstructured":"Merler M, Wu H, Uceda-Sosa R et\u00a0al (2016) Snap, Eat, RepEat: A Food Recognition Engine for Dietary Logging. In: Proc. international workshop on multimedia assisted dietary management, pp 31\u201340. https:\/\/doi.org\/10.1145\/2986035.2986036","DOI":"10.1145\/2986035.2986036"},{"issue":"5","key":"19161_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3329168","volume":"52","author":"W Min","year":"2019","unstructured":"Min W, Jiang S, Liu L et al (2019) A Survey on Food Computing. ACM Comput Surv 52(5):1\u201336. https:\/\/doi.org\/10.1145\/3329168","journal-title":"ACM Comput Surv"},{"key":"19161_CR45","doi-asserted-by":"publisher","unstructured":"Min W, Liu L, Luo Z et\u00a0al (2019b) Ingredient-Guided Cascaded Multi-Attention Network for Food Recognition. In: Proc. ACM international conference on multimedia, pp 1331\u20131339. https:\/\/doi.org\/10.1145\/3343031.3350948","DOI":"10.1145\/3343031.3350948"},{"key":"19161_CR46","doi-asserted-by":"publisher","unstructured":"Min W, Liu L, Wang Z et\u00a0al (2020) ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network. In: Proc. ACM international conference on multimedia, pp 393\u2013401. https:\/\/doi.org\/10.1145\/3394171.3414031","DOI":"10.1145\/3394171.3414031"},{"issue":"8","key":"19161_CR47","doi-asserted-by":"publisher","first-page":"9932","DOI":"10.1109\/TPAMI.2023.3237871","volume":"45","author":"W Min","year":"2023","unstructured":"Min W, Wang Z, Liu Y et al (2023) Large Scale Visual Food Recognition. IEEE Trans Pattern Anal Mach Intell 45(8):9932\u20139949. https:\/\/doi.org\/10.1109\/TPAMI.2023.3237871","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"19161_CR48","doi-asserted-by":"publisher","unstructured":"Morales R, Quispe J, Aguilar E (2023) Exploring Multi-food Detection Using Deep Learning-based Algorithms. In: 2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS), pp 1\u20137. https:\/\/doi.org\/10.1109\/ICPRS58416.2023.10179037","DOI":"10.1109\/ICPRS58416.2023.10179037"},{"key":"19161_CR49","doi-asserted-by":"publisher","unstructured":"Myers A, Johnston N, Rathod V et\u00a0al (2015) Im2Calories: Towards an Automated Mobile Vision Food Diary. In: Proc. IEEE international conference on computer vision, pp 1233\u20131241. https:\/\/doi.org\/10.1109\/ICCV.2015.146","DOI":"10.1109\/ICCV.2015.146"},{"key":"19161_CR50","doi-asserted-by":"publisher","unstructured":"Popovski G, Seljak BK, Eftimov T (2019) FoodBase Corpus: A New Resource of Annotated Food Entities. Database 2019:baz121. https:\/\/doi.org\/10.1093\/database\/baz121","DOI":"10.1093\/database\/baz121"},{"key":"19161_CR51","doi-asserted-by":"publisher","unstructured":"Pouladzadeh P, Shirmohammadi S, Yassine A (2014) Using Graph Cut Segmentation for Food Calorie Measurement. In: Proc. IEEE international symposium on medical measurements and applications, pp 1\u20136. https:\/\/doi.org\/10.1109\/MeMeA.2014.6860137","DOI":"10.1109\/MeMeA.2014.6860137"},{"key":"19161_CR52","doi-asserted-by":"publisher","unstructured":"Qiu J, Lo FPW, Sun Y et\u00a0al (2019) Mining Discriminative Food Regions for Accurate Food Recognition. In: Proc. British machine vision conference, p 158. https:\/\/doi.org\/10.48550\/arXiv.2207.03692","DOI":"10.48550\/arXiv.2207.03692"},{"key":"19161_CR53","doi-asserted-by":"publisher","unstructured":"Rich J, Haddadi H, Hospedales TM (2016) Towards Bottom-up Analysis of Social Food. In: Proc. international conference on digital health conference, pp 111\u2013120. https:\/\/doi.org\/10.1145\/2896338.2897734","DOI":"10.1145\/2896338.2897734"},{"key":"19161_CR54","doi-asserted-by":"publisher","unstructured":"Romero-Tapiador S, Lacruz-Pleguezuelos B, Tolosana R et\u00a0al (2023a) AI4FoodDB: A Database for Personalized e-Health Nutrition and Lifestyle through Wearable Devices and Artificial Intelligence. Database 2023:baad049. https:\/\/doi.org\/10.1093\/database\/baad049","DOI":"10.1093\/database\/baad049"},{"key":"19161_CR55","doi-asserted-by":"publisher","first-page":"112199","DOI":"10.1109\/ACCESS.2023.3322770","volume":"11","author":"S Romero-Tapiador","year":"2023","unstructured":"Romero-Tapiador S, Tolosana R, Morales A et al (2023) AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours. IEEE Access 11:112199\u2013112211. https:\/\/doi.org\/10.1109\/ACCESS.2023.3322770","journal-title":"IEEE Access"},{"key":"19161_CR56","doi-asserted-by":"publisher","unstructured":"Sahoo D, Hao W, Ke S et\u00a0al (2019) FoodAI: Food Image Recognition Via Deep Learning for Smart Food Logging. In: Proc. ACM SIGKDD international conference on knowledge discovery & data mining, pp 2260\u20132268. https:\/\/doi.org\/10.1145\/3292500.3330734","DOI":"10.1145\/3292500.3330734"},{"key":"19161_CR57","doi-asserted-by":"publisher","unstructured":"Singla A, Yuan L, Ebrahimi T (2016) Food\/Non-Food Image Classification and Food Categorization Using Pre-Trained GoogLeNet Model. In: Proc. international workshop on multimedia assisted dietary management, pp 3\u201411. https:\/\/doi.org\/10.1145\/2986035.2986039","DOI":"10.1145\/2986035.2986039"},{"key":"19161_CR58","doi-asserted-by":"publisher","unstructured":"Sociedad Espa\u00f1ola De Nutrici\u00f3n Comunitaria (2016) Gu\u00edas Alimentarias para la Poblaci\u00f3n Espa\u00f1ola (SENC, Diciembre 2016); la Nueva Pir\u00e1mide de la Alimentaci\u00f3n Saludablea. Nutrici\u00f3n hospitalaria 33(8):1\u201348. https:\/\/doi.org\/10.20960\/nh.827","DOI":"10.20960\/nh.827"},{"key":"19161_CR59","doi-asserted-by":"publisher","first-page":"35370","DOI":"10.1109\/ACCESS.2019.2904519","volume":"7","author":"MA Subhi","year":"2019","unstructured":"Subhi MA, Ali SH, Mohammed MA (2019) Vision-Based Approaches for Automatic Food Recognition and Dietary Assessment: A Survey. IEEE Access 7:35370\u201335381. https:\/\/doi.org\/10.1109\/ACCESS.2019.2904519","journal-title":"IEEE Access"},{"key":"19161_CR60","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y et\u00a0al (2015) Going deeper with convolutions. In: Proc. IEEE conference on computer vision and pattern recognition, pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"19161_CR61","doi-asserted-by":"publisher","unstructured":"Tammachat N, Pantuwong N (2014) Calories Analysis of Food Intake Using Image Recognition. In: Proc. international conference on information technology and electrical engineering, pp 1\u20134. https:\/\/doi.org\/10.1109\/ICITEED.2014.7007901","DOI":"10.1109\/ICITEED.2014.7007901"},{"key":"19161_CR62","doi-asserted-by":"publisher","unstructured":"Tan M, Le Q (2021) Efficientnetv2: Smaller Models and Faster Training. In: International conference on machine learning, pp 10096\u201310106. https:\/\/doi.org\/10.48550\/arXiv.2104.00298","DOI":"10.48550\/arXiv.2104.00298"},{"key":"19161_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104673","volume":"110","author":"R Tolosana","year":"2022","unstructured":"Tolosana R, Romero-Tapiador S, Vera-Rodriguez R et al (2022) DeepFakes Detection Across Generations: Analysis of Facial Regions, Fusion, and Performance Evaluation. Eng Appl Artif Intell 110:104673. https:\/\/doi.org\/10.1016\/j.engappai.2022.104673","journal-title":"Eng Appl Artif Intell"},{"key":"19161_CR64","doi-asserted-by":"publisher","unstructured":"Waltner G, Schwarz M, Ladst\u00e4tter S, et\u00a0al (2017) Personalized Dietary Self-Management using Mobile Vision-based Assistance. In: Proc. workshop on multimedia assisted dietary management, pp 385\u2013393. https:\/\/doi.org\/10.1007\/978-3-319-70742-6_36","DOI":"10.1007\/978-3-319-70742-6_36"},{"key":"19161_CR65","doi-asserted-by":"publisher","unstructured":"Wang X, Kumar D, Thome N et\u00a0al (2015) Recipe Recognition with Large Multimodal Food Dataset. In: Proc. IEEE international conference on multimedia & expo workshops, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICMEW.2015.7169757","DOI":"10.1109\/ICMEW.2015.7169757"},{"key":"19161_CR66","volume-title":"The Double Burden of Malnutrition: Policy Brief","author":"World Health Organization","year":"2016","unstructured":"World Health Organization (2016) The Double Burden of Malnutrition: Policy Brief. World Health Organization, Tech. rep"},{"key":"19161_CR67","doi-asserted-by":"publisher","unstructured":"Xu R, Herranz L, Jiang S et al (2015) Geolocalized Modeling for Dish Recognition. IEEE Trans Multimed 17(8):1187\u20131199. https:\/\/doi.org\/10.1109\/TMM.2015.2438717","DOI":"10.1109\/TMM.2015.2438717"},{"key":"19161_CR68","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-15611-7","volume-title":"A Review Study on Digital Twins with Artificial Intelligence and Internet of Things: Concepts","author":"SM Zayed","year":"2023","unstructured":"Zayed SM, Attiya GM, El-Sayed A et al (2023) A Review Study on Digital Twins with Artificial Intelligence and Internet of Things: Concepts. Multimed Tools Appl, Opportunities, Challenges, Tools and Future Scope. https:\/\/doi.org\/10.1007\/s11042-023-15611-7"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19161-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19161-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19161-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T01:48:02Z","timestamp":1738115282000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19161-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,18]]},"references-count":68,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["19161"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19161-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,18]]},"assertion":[{"value":"4 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}