Leveraging Context to Support Automated Food Recognition in Restaurants

Bettadapura, Vinay; Thomaz, Edison; Parnami, Aman; Abowd, Gregory D.; Essa, Irfan

doi:10.1109/wacv.2015.83

Cited by 93 publications

(47 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the results from several individual classifiers are combined to improve the accuracy, which for the system is about 80%. Similar results were obtained in the work [3]. The authors of the work [4] used a combination of deformable part-based and a texture model.…”

Section: Introductionsupporting

confidence: 80%

Shape and colour recognition of dishes for the purpose of customer service process automation in a self-service canteen

Kryjak

Krol

2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-In the article a vision system for shape and colour recognition of dishes (plates, bowls, mugs), which can be used to automate the process of customer service in a self-service canteen is described. In consists of three basic components: object segmentation using so-called background model subtraction, shape recognition using geometric invariant moments and SVM classifier, as well as colour recognition using a Gaussian model. In addition, recognition in case of close or abut objects using a distance transform like approach is presented. The solution was evaluated on a dedicated test stand with controlled LED lightning. A 98% accuracy was obtained on over 100 test images, which indicates that the solution could be used in business practise.

show abstract

Section: Introductionsupporting

confidence: 80%

Shape and colour recognition of dishes for the purpose of customer service process automation in a self-service canteen

Kryjak

Krol

2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…They achieved a classification accuracy of 28.2% on 61 food categories which was a subset of Pittsburgh dataset [12]. Bettadapura et al [14] used combined 6-feature descriptors (2 color-based and 4 SIFT-based) and SMK-MKL Sequential Minimal Optimization to train an SVM classifier. They experimented on a dataset consisting of 3750 food images of 75 categories (50 images per category) and reported an accuracy of 63.33% on their test dataset.…”

Section: Food Image Recognitionmentioning

confidence: 99%

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Singla

Lin

Ebrahimi

2016

Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management

169

View full text Add to dashboard Cite

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.

show abstract

“…Lastly, the self-tracking method used in this intervention was traditional self-reported food journaling, which imposes a high burden on participants [64]. Recent automated food recognition technologies rely on wearable cameras or phones to capture food photos and leverage computer vision techniques to analyze food ingredients [65,66]. Another alternative method is to use an in-the-moment photo as a lightweight food journal to reduce user effort [67].…”

Section: Discussionmentioning

confidence: 99%

Online Self-Tracking Groups to Increase Fruit and Vegetable Intake: A Small-Scale Study on Mechanisms of Group Effect on Behavior Change

Meng¹,

Peng²,

Shin³

et al. 2017

J Med Internet Res

View full text Add to dashboard Cite

BackgroundWeb-based interventions with a self-tracking component have been found to be effective in promoting adults’ fruit and vegetable consumption. However, these interventions primarily focus on individual- rather than group-based self-tracking. The rise of social media technologies enables sharing and comparing self-tracking records in a group context. Therefore, we developed an online group-based self-tracking program to promote fruit and vegetable consumption.ObjectiveThis study aims to examine (1) the effectiveness of online group-based self-tracking on fruit and vegetable consumption and (2) characteristics of online self-tracking groups that make the group more effective in promoting fruit and vegetable consumption in early young adults.MethodsDuring a 4-week Web-based experiment, 111 college students self-tracked their fruit and vegetable consumption either individually (ie, the control group) or in an online group characterized by a 2 (demographic similarity: demographically similar vs demographically diverse) × 2 (social modeling: incremental change vs ideal change) experimental design. Each online group consisted of one focal participant and three confederates as group members or peers, who had their demographics and fruit and vegetable consumption manipulated to create the four intervention groups. Self-reported fruit and vegetable consumption were assessed using the Food Frequency Questionnaire at baseline and after the 4-week experiment.ResultsParticipants who self-tracked their fruit and vegetable consumption collectively with other group members consumed more fruits and vegetables than participants who self-tracked individually (P=.01). The results did not show significant main effects of demographic similarity (P=.32) or types of social modeling (P=.48) in making self-tracking groups more effective in promoting fruit and vegetable consumption. However, additional analyses revealed the main effect of performance discrepancy (ie, difference in fruit and vegetable consumption between a focal participant and his/her group members during the experiment), such that participants who had a low performance discrepancy from other group members had greater fruit and vegetable consumption than participants who had a high performance discrepancy from other group members (P=.002). A mediation test showed that low performance discrepancy led to greater downward contrast (b=–0.78, 95% CI –2.44 to –0.15), which in turn led to greater fruit and vegetable consumption.ConclusionsOnline self-tracking groups were more effective than self-tracking alone in promoting fruit and vegetable consumption for early young adults. Low performance discrepancy from other group members lead to downward contrast, which in turn increased participants’ fruit and vegetable consumption over time. The study highlighted social comparison processes in online groups that allow for sharing personal health information. Lastly, given the small scale of this study, nonsignificant results with small effect sizes might be subject to bias.

show abstract

Leveraging Context to Support Automated Food Recognition in Restaurants

Cited by 93 publications

References 24 publications

Shape and colour recognition of dishes for the purpose of customer service process automation in a self-service canteen

Shape and colour recognition of dishes for the purpose of customer service process automation in a self-service canteen

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Online Self-Tracking Groups to Increase Fruit and Vegetable Intake: A Small-Scale Study on Mechanisms of Group Effect on Behavior Change

Contact Info

Product

Resources

About