Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management最新文献

Learning Multi-Subset of Classes for Fine-Grained Food Recognition 学习类的多子集用于细粒度食物识别

Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management Pub Date : 2022-10-10 DOI: 10.1145/3552484.3555754

Javier Ródenas, Bhalaji Nagarajan, Marc Bolaños, P. Radeva

{"title":"Learning Multi-Subset of Classes for Fine-Grained Food Recognition","authors":"Javier Ródenas, Bhalaji Nagarajan, Marc Bolaños, P. Radeva","doi":"10.1145/3552484.3555754","DOIUrl":"https://doi.org/10.1145/3552484.3555754","url":null,"abstract":"Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are more difficult to discriminate based on Multi-Subsets learning. Using a pre-trained network, we organize classes in multiple subsets using a clustering technique. Later, we embed these subsets in a multi-head model structure. This structure has three distinguishable parts. First, we use several shared blocks to learn the generalized representation of the data. Second, we use multiple specialized blocks focusing on specific subsets that are difficult to distinguish. Lastly, we use a fully connected layer to weight the different subsets in an end-to-end manner by combining the neuron outputs. We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets. Our method was successful in learning the confused classes better and we outperformed the state-of-the-art on the three datasets.","PeriodicalId":111648,"journal":{"name":"Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114968583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

mediPiatto: Using AI to Assess and Improve Mediterranean Diet Adherence mediatto:使用人工智能来评估和改善地中海饮食依从性

Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management Pub Date : 2022-10-10 DOI: 10.1145/3552484.3554368

Arindam Ghosh

{"title":"mediPiatto: Using AI to Assess and Improve Mediterranean Diet Adherence","authors":"Arindam Ghosh","doi":"10.1145/3552484.3554368","DOIUrl":"https://doi.org/10.1145/3552484.3554368","url":null,"abstract":"Numerous studies have demonstrated the benefits of Mediterranean Diet Adherence (MDA) to improved long-term weight loss outcomes, positive effects on cardiovascular health, and decrease in complications among diabetic patients. However, manual assessment of MDA on a regular basis is challenging, and a convenient method of such evaluation is needed for mass adoption. The goal of the mediPiatto research project was to develop an AI-based end-to-end automatic system for evaluation and improvement of MDA from meal log images. The developed system was embedded into a smartphone application for meal tracking. A 4-week feasibility study was conducted with 24 participants where a weekly report with a score quantifying their adherence was sent to them. A comparison of the system-generated MDA score of four users with that calculated by an expert dietitian showed a mean difference of 3.5%. A self-reported food frequency questionnaire (FFQ) - used as a self-measurement of a person's compliance with the MD - showed that 19 out of 24 participants had an overall increase in the score over the period of the study. An end-of-study survey yielded overall positive feedback from the participants with 20 out of 24 reporting that they would be interested in incorporating the system in their daily lives.","PeriodicalId":111648,"journal":{"name":"Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management","volume":"503 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128293998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Quest towards Automated Dietary Monitoring & Intervention in Free-living 对自由生活中自动饮食监测和干预的探索

Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management Pub Date : 2022-10-10 DOI: 10.1145/3552484.3554984

O. Amft

引用次数: 1

World Food Atlas for Food Navigation 世界食品导航地图集

Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management Pub Date : 2022-10-10 DOI: 10.1145/3552484.3555748

Ali Rostami, N. Nagesh, Amir Rahmani, Ramesh C. Jain

{"title":"World Food Atlas for Food Navigation","authors":"Ali Rostami, N. Nagesh, Amir Rahmani, Ramesh C. Jain","doi":"10.1145/3552484.3555748","DOIUrl":"https://doi.org/10.1145/3552484.3555748","url":null,"abstract":"Food plays a central role in agriculture, public wellness, public health, culinary art, and culture. Food-related data is available in varying formats and with different access levels ranging from private datasets to publicly downloadable data. Every food-related query, in principle, is a food recommendation problem. We analyze the components of a food recommendation and its requirements. We demonstrate the effectiveness of having access to worldwide food data from divergent aspects for answering food- and health-related queries that would otherwise be expensive and require specialized domain expertise. We present the World Food Atlas (WFA): An open-source platform for different stakeholders in the food ecosystem to share their data on a global data hub with a singular point of access. The world food atlas contains the availability and inter-connectivity of food and its effects in various forms. We gather real-world questions by partnering with nutritionists, dietitians, and doctors. We categorize the practical food queries to construct requirement tables and develop a novel schema to satisfy the requirement table to model the world food atlas. Finally, we demonstrate how food and lifestyle navigation systems can use the world food atlas to enable personalized and context-driven solutions to person-entity-context queries.","PeriodicalId":111648,"journal":{"name":"Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130265033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Text-based Image Editing for Food Images with CLIP 基于文本的图像编辑与CLIP食品图像

Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management Pub Date : 2022-10-10 DOI: 10.1145/3552484.3555751

Kohei Yamamoto, Keiji Yanai

引用次数: 0

AI-Assisted Food Intake Activity Recognition Using 3D mmWave Radars 使用3D毫米波雷达的人工智能辅助食物摄入活动识别

Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management Pub Date : 2022-10-10 DOI: 10.1145/3552484.3555753

Yi-Hung Wu, Yuanjie Chen, S. Shirmohammadi, Cheng-Hsin Hsu

引用次数: 5

Automatic Classification of High vs. Low Individual Nutrition Literacy Levels from Loyalty Card Data in Switzerland 根据瑞士忠诚卡数据对高与低个人营养素养水平的自动分类

Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management Pub Date : 2022-10-10 DOI: 10.1145/3552484.3555744

Marc Blöchlinger, Jing Wu, Simon Mayer, K. Fuchs, Melanie Stoll, L. Bally

{"title":"Automatic Classification of High vs. Low Individual Nutrition Literacy Levels from Loyalty Card Data in Switzerland","authors":"Marc Blöchlinger, Jing Wu, Simon Mayer, K. Fuchs, Melanie Stoll, L. Bally","doi":"10.1145/3552484.3555744","DOIUrl":"https://doi.org/10.1145/3552484.3555744","url":null,"abstract":"The increasingly prevalent diet-related non-communicable diseases (NCDs) constitute a modern health pandemic. Higher nutrition literacy (NL) correlates with healthier diets, which in turn has favorable effects on NCDs. Assessing and classifying people's NL is helpful in tailoring the level of education required for disease self-management/empowerment and adequate treatment strategy selection. With recently introduced regulation in the European Union and beyond, it has become easier to leverage loyalty card data and enrich it with nutrition information about bought products. We present a novel system that utilizes such data to classify individuals into high- and low- NL classes, using well-known machine learning (ML) models, thereby permitting for instance better targeting of educational measures to support the population-level management of NCDs. An online survey (n = 779) was conducted to assess individual NL levels and divide participants into high- and low- NL groups. Our results show that there are significant differences in NL between male and female, as well as between overweight and non-overweight individuals. No significant differences were found for other demographic parameters that were investigated. Next, the loyalty card data of participants (n = 11) was collected from two leading Swiss retailers with the consent of participants and a ML system was trained to predict high or low NL for these individuals. Our best ML model, which utilizes the XGBoost algorithm and monthly aggregated baskets, achieved a Macro-F1-score of .89 at classifying NL. We hence show the feasibility of identifying individual NL levels based on household loyalty card data leveraging ML models, however due to the small sample size, the results need to be further verified with a larger sample size.","PeriodicalId":111648,"journal":{"name":"Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123789577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Real Scale Hungry Networks: Real Scale 3D Reconstruction of a Dish and a Plate using Implicit Function and a Single RGB-D Image 真实尺度饥饿网络:使用隐式函数和单个RGB-D图像对盘子和盘子进行真实尺度三维重建

Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management Pub Date : 2022-10-10 DOI: 10.1145/3552484.3555750

Shu Naritomi, Keiji Yanai

引用次数: 0

Simulating Personal Food Consumption Patterns using a Modified Markov Chain 利用改进的马尔可夫链模拟个人食品消费模式

Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management Pub Date : 2022-08-13 DOI: 10.1145/3552484.3555747

Xinyue Pan, Jiangpeng He, Andrew Peng, F. Zhu

{"title":"Simulating Personal Food Consumption Patterns using a Modified Markov Chain","authors":"Xinyue Pan, Jiangpeng He, Andrew Peng, F. Zhu","doi":"10.1145/3552484.3555747","DOIUrl":"https://doi.org/10.1145/3552484.3555747","url":null,"abstract":"Food image classification serves as the foundation of image-based dietary assessment to predict food categories. Since there are many different food classes in real life, conventional models cannot achieve sufficiently high accuracy. Personalized classifiers aim to largely improve the accuracy of food image classification for each individual. However, a lack of public personal food consumption data proves to be a challenge for training such models. To address this issue, we propose a novel framework to simulate personal food consumption data patterns, leveraging the use of a modified Markov chain model and self-supervised learning. Our method is capable of creating an accurate future data pattern from a limited amount of initial data, and our simulated data patterns can be closely correlated with the initial data pattern. Furthermore, we use Dynamic Time Warping distance and Kullback-Leibler divergence as metrics to evaluate the effectiveness of our method on the public Food-101 dataset. Our ex- perimental results demonstrate promising performance compared with random simulation and the original Markov chain method.","PeriodicalId":111648,"journal":{"name":"Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130310697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Chewing Detection from Commercial Smart-glasses 商用智能眼镜的咀嚼检测

Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management Pub Date : 2022-08-11 DOI: 10.1145/3552484.3555746

Vasileios Papapanagiotou, A. Liapi, A. Delopoulos

{"title":"Chewing Detection from Commercial Smart-glasses","authors":"Vasileios Papapanagiotou, A. Liapi, A. Delopoulos","doi":"10.1145/3552484.3555746","DOIUrl":"https://doi.org/10.1145/3552484.3555746","url":null,"abstract":"Automatic dietary monitoring has progressed significantly during the last years, offering a variety of solutions, both in terms of sensors and algorithms as well as in terms of what aspect or parameters of eating behavior are measured and monitored. Automatic detection of eating based on chewing sounds has been studied extensively, however, it requires a microphone to be mounted on the subject's head for capturing the relevant sounds. In this work, we evaluate the feasibility of using an off-the-shelf commercial device, the Razer Anzu smart-glasses, for automatic chewing detection. The smart-glasses are equipped with stereo speakers and microphones that communicate with smart-phones via Bluetooth. The microphone placement is not optimal for capturing chewing sounds, however, we find that it does not significantly affect the detection effectiveness. We apply an algorithm from literature with some adjustments on a challenging dataset that we have collected in house. Leave-one-subject-out experiments yield promising results, with an F1-score of $0.96$ for the best case of duration-based evaluation of eating time.","PeriodicalId":111648,"journal":{"name":"Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132927106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2