发布求助
文献互助 智能选刊 最新文献

MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...最新文献

筛选
英文 中文
Session details: Keynote Address 会议详情:主题演讲
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam... Pub Date : 2016-10-16 DOI: 10.1145/3257997
Thomas Mensink
{"title":"Session details: Keynote Address","authors":"Thomas Mensink","doi":"10.1145/3257997","DOIUrl":"https://doi.org/10.1145/3257997","url":null,"abstract":"","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77006797","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: 0
Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model 使用预训练GoogLeNet模型的食品/非食品图像分类和食品分类
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam... Pub Date : 2016-10-16 DOI: 10.1145/2986035.2986039
Ashutosh Singla, Lin Yuan, T. Ebrahimi
{"title":"Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model","authors":"Ashutosh Singla, Lin Yuan, T. Ebrahimi","doi":"10.1145/2986035.2986039","DOIUrl":"https://doi.org/10.1145/2986035.2986039","url":null,"abstract":"Recent past has seen a lot of developments in the field of image-based dietary assessment. Food image classification and recognition are crucial steps for dietary assessment. In the last couple of years, advancements in the deep learning and convolutional neural networks proved to be a boon for the image classification and recognition tasks, specifically for food recognition because of the wide variety of food items. In this paper, we report experiments on food/non-food classification and food recognition using a GoogLeNet model based on deep convolutional neural network. The experiments were conducted on two image datasets created by our own, where the images were collected from existing image datasets, social media, and imaging devices such as smart phone and wearable cameras. Experimental results show a high accuracy of 99.2% on the food/non-food classification and 83.6% on the food category recognition.","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87852236","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: 157
Session details: Oral Paper Session 3 会议细节:口头论文会议3
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam... Pub Date : 2016-10-16 DOI: 10.1145/3257998
Keiji Yanai
{"title":"Session details: Oral Paper Session 3","authors":"Keiji Yanai","doi":"10.1145/3257998","DOIUrl":"https://doi.org/10.1145/3257998","url":null,"abstract":"","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91502882","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: 0
Session details: Oral Paper Session 2 会议细节:口头论文会议2
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam... Pub Date : 2016-10-16 DOI: 10.1145/3257996
S. Mougiakakou
{"title":"Session details: Oral Paper Session 2","authors":"S. Mougiakakou","doi":"10.1145/3257996","DOIUrl":"https://doi.org/10.1145/3257996","url":null,"abstract":"","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91048171","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: 0
Food Image Recognition Using Very Deep Convolutional Networks 使用非常深卷积网络的食物图像识别
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam... Pub Date : 2016-10-16 DOI: 10.1145/2986035.2986042
Hamid Hassannejad, G. Matrella, P. Ciampolini, I. D. Munari, M. Mordonini, S. Cagnoni
{"title":"Food Image Recognition Using Very Deep Convolutional Networks","authors":"Hamid Hassannejad, G. Matrella, P. Ciampolini, I. D. Munari, M. Mordonini, S. Cagnoni","doi":"10.1145/2986035.2986042","DOIUrl":"https://doi.org/10.1145/2986035.2986042","url":null,"abstract":"We evaluated the effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 layers. In this study, we fine-tuned this architecture for classifying food images from three well-known food image datasets: ETH Food-101, UEC FOOD 100, and UEC FOOD 256. On these datasets we achieved, respectively, 88.28%, 81.45%, and 76.17% as top-1 accuracy and 96.88%, 97.27%, and 92.58% as top-5 accuracy. To the best of our knowledge, these results significantly improve the best published results obtained on the same datasets, while requiring less computation power, since the number of parameters and the computational complexity are much smaller than the competitors?. Because of this, even if it is still rather large, the deep network based on this architecture appears to be at least closer to the requirements for mobile systems.","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"10 113 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73241381","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: 161
Foodness Proposal for Multiple Food Detection by Training of Single Food Images 基于单一食物图像训练的多食物检测方法
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam... Pub Date : 2016-10-16 DOI: 10.1145/2986035.2986043
Wataru Shimoda, Keiji Yanai
{"title":"Foodness Proposal for Multiple Food Detection by Training of Single Food Images","authors":"Wataru Shimoda, Keiji Yanai","doi":"10.1145/2986035.2986043","DOIUrl":"https://doi.org/10.1145/2986035.2986043","url":null,"abstract":"We propose a CNN-based \"food-ness\" proposal method which requires neither pixel-wise annotation nor bounding box annotation. Some proposal methods have been proposed to detect regions with high \"object-ness\" so far. However, many of them generated a large number of candidates to raise the recall rate. Considering the recent advent of the deeper CNN, these methods to generate a large number of proposals have difficulty in processing time for practical use. Meanwhile, a fully convolutional network (FCN) was proposed the network of which localizes target objects directly. FCN saves computational cost, although FCN is essentially equivalent to the sliding window search. This approach made large progress and achieved significant success in various tasks. Then, in this paper we propose an intermediate approach between the traditional proposal approach and the fully convolutional approach. Especially we propose a novel proposal method which generates high \"food-ness\" regions by fully convolutional networks and back-propagation based approach with training food images gathered from the Web.","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81965615","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: 15
Snap, Eat, RepEat: A Food Recognition Engine for Dietary Logging Snap, Eat, RepEat:一个用于饮食记录的食物识别引擎
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam... Pub Date : 2016-10-16 DOI: 10.1145/2986035.2986036
Michele Merler, Hui Wu, Rosario A. Uceda-Sosa, Q. Nguyen, John R. Smith
{"title":"Snap, Eat, RepEat: A Food Recognition Engine for Dietary Logging","authors":"Michele Merler, Hui Wu, Rosario A. Uceda-Sosa, Q. Nguyen, John R. Smith","doi":"10.1145/2986035.2986036","DOIUrl":"https://doi.org/10.1145/2986035.2986036","url":null,"abstract":"We present a system to assist users in dietary logging habits, which performs food recognition from pictures snapped on their phone in two different scenarios. In the first scenario, called \"Food in context\", we exploit the GPS information of a user to determine which restaurant they are having a meal at, therefore restricting the categories to recognize to the set of items in the menu. Such context allows us to also report precise calories information to the user about their meal, since restaurant chains tend to standardize portions and provide the dietary information of each meal. In the second scenario, called \"Foods in the wild\" we try to recognize a cooked meal from a picture which could be snapped anywhere. We perform extensive experiments on food recognition on both scenarios, demonstrating the feasibility of our approach at scale, on a newly introduced dataset with 105K images for 500 food categories.","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91470094","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: 36
Performance Evaluation Methods of Computer Vision Systems for Meal Assessment 用于膳食评估的计算机视觉系统性能评估方法
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam... Pub Date : 2016-10-16 DOI: 10.1145/2986035.2986045
M. Anthimopoulos, Joachim Dehais, S. Mougiakakou
{"title":"Performance Evaluation Methods of Computer Vision Systems for Meal Assessment","authors":"M. Anthimopoulos, Joachim Dehais, S. Mougiakakou","doi":"10.1145/2986035.2986045","DOIUrl":"https://doi.org/10.1145/2986035.2986045","url":null,"abstract":"Several systems have been proposed for the automatic food intake assessment and dietary support by analyzing meal images captured by smartphones. A typical system consists of computational stages that detect/segment the existing foods, recognize each of them, compute their volume, and finally estimate the corresponding nutritional information. Although this newborn field has made remarkable progress over the last years, the lack of standardized datasets and established evaluation frameworks has made difficult the comparison between methods and eventually prevented the formal definition of the problem. In this paper, we present an overview of the datasets and protocols used for evaluating the computer vision stages of the proposed automatic meal assessment systems.","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81391392","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
Food Search Based on User Feedback to Assist Image-based Food Recording Systems 基于用户反馈的食物搜索辅助基于图像的食物记录系统
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam... Pub Date : 2016-10-16 DOI: 10.1145/2986035.2986037
Sosuke Amano, Shota Horiguchi, K. Aizawa, Kazuki Maeda, Masanori Kubota, Makoto Ogawa
{"title":"Food Search Based on User Feedback to Assist Image-based Food Recording Systems","authors":"Sosuke Amano, Shota Horiguchi, K. Aizawa, Kazuki Maeda, Masanori Kubota, Makoto Ogawa","doi":"10.1145/2986035.2986037","DOIUrl":"https://doi.org/10.1145/2986035.2986037","url":null,"abstract":"Food diaries or diet journals are thought to be effective for improving the dietary lives of users. One important challenge in this field involves assisting users in recording their daily food intake. In recent years, food image recognition has attracted a considerable amount of research interest as a new technology to help record users 'food intake. However, since there are so many types of food, and it is unrealistic to expect a system to recognize all foods. In this paper, we propose an optimal combination of image recognition and interactive search in order to record users 'intake of food. The image recognition generates a list of candidate names for a given food picture. The user chooses the closest name to the meal, which triggers an associative food search based on food contents, such as ingredients. We show the proposed system is efficient to assist users maintain food journals.","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"122 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81090936","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
An Automatic Calorie Estimation System of Food Images on a Smartphone 智能手机上的食物图像自动卡路里估算系统
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam... Pub Date : 2016-10-16 DOI: 10.1145/2986035.2986040
Koichi Okamoto, Keiji Yanai
{"title":"An Automatic Calorie Estimation System of Food Images on a Smartphone","authors":"Koichi Okamoto, Keiji Yanai","doi":"10.1145/2986035.2986040","DOIUrl":"https://doi.org/10.1145/2986035.2986040","url":null,"abstract":"In recent years, due to a rise in healthy thinking on eating, many people take care of their eating habits, and some people record daily diet regularly. To assist them, many mobile applications for recording everyday meals have been released so far. Some of them employ food image recognition which can estimate not only food names but also food calorie. However, most of such applications have some problems especially on their usability. Then, in this paper, we propose a novel single-image-based food calorie estimation system which runs on a smartphone as a standalone application without external recognition servers. The proposed system carries out food region segmentation, food region categorization, and calorie estimation automatically. By the experiments and the user study on the proposed system, the effectiveness of the proposed system was confirmed.","PeriodicalId":91925,"journal":{"name":"MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79629910","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: 57
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信