{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepfood-deep-learning-based-food-image","title":"DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment","arxiv_id":"1606.05675","date":"2016-06-17","proceeding":null,"authors":["Chang Liu","Yu Cao","Yan Luo","Guanling Chen","Vinod Vokkarane","Yunsheng Ma"],"abstract":"Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were\noverweight. Of these, over 600 million were obese. Accurately documenting\ndietary caloric intake is crucial to manage weight loss, but also presents\nchallenges because most of the current methods for dietary assessment must rely\non memory to recall foods eaten. The ultimate goal of our research is to\ndevelop computer-aided technical solutions to enhance and improve the accuracy\nof current measurements of dietary intake. Our proposed system in this paper\naims to improve the accuracy of dietary assessment by analyzing the food images\ncaptured by mobile devices (e.g., smartphone). The key technique innovation in\nthis paper is the deep learning-based food image recognition algorithms.\nSubstantial research has demonstrated that digital imaging accurately estimates\ndietary intake in many environments and it has many advantages over other\nmethods. However, how to derive the food information (e.g., food type and\nportion size) from food image effectively and efficiently remains a challenging\nand open research problem. We propose a new Convolutional Neural Network\n(CNN)-based food image recognition algorithm to address this problem. We\napplied our proposed approach to two real-world food image data sets (UEC-256\nand Food-101) and achieved impressive results. To the best of our knowledge,\nthese results outperformed all other reported work using these two data sets.\nOur experiments have demonstrated that the proposed approach is a promising\nsolution for addressing the food image recognition problem. Our future work\nincludes further improving the performance of the algorithms and integrating\nour system into a real-world mobile and cloud computing-based system to enhance\nthe accuracy of current measurements of dietary intake.","url_abs":"http://arxiv.org/abs/1606.05675v1","url_pdf":"http://arxiv.org/pdf/1606.05675v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepfood-deep-learning-based-food-image","repo_url":"https://github.com/deercoder/DeepFood","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cloud-computing","task_name":"Cloud Computing"},{"task_slug":"fine-grained-image-recognition","task_name":"Fine-Grained Image Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.05675","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}