SlowDeepFood: a Food Computing Framework for Regional Gastronomy

Simple item page
dc.contributor.authorGilal, Nauman Ullahen_US
dc.contributor.authorAl-Thelaya, Khaleden_US
dc.contributor.authorSchneider, Jensen_US
dc.contributor.authorShe, Jamesen_US
dc.contributor.authorAgus, Marcoen_US
dc.contributor.editorFrosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanueleen_US
dc.date.accessioned2021-10-25T11:53:38Z
dc.date.available2021-10-25T11:53:38Z
dc.date.issued2021
dc.description.abstractFood computing recently emerged as a stand-alone research field, in which artificial intelligence, deep learning, and data science methodologies are applied to the various stages of food production pipelines. Food computing may help end-users in maintaining healthy and nutritious diets by alerting of high caloric dishes and/or dishes containing allergens. A backbone for such applications, and a major challenge, is the automated recognition of food by means of computer vision. It is therefore no surprise that researchers have compiled various food data sets and paired them with well-performing deep learning architecture to perform said automatic classification. However, local cuisines are tied to specific geographic origins and are woefully underrepresented in most existing data sets. This leads to a clear gap when it comes to food computing on regional and traditional dishes. While one might argue that standardized data sets of world cuisine cover the majority of applications, such a stance would neglect systematic biases in data collection. It would also be at odds with recent initiatives such as SlowFood, seeking to support local food traditions and to preserve local contributions to the global variation of food items. To help preserve such local influences, we thus present a full end-to-end food computing network that is able to: (i) create custom image data sets semi-automatically that represent traditional dishes; (ii) train custom classification models based on the EfficientNet family using transfer learning; (iii) deploy the resulting models in mobile applications for real-time inference of food images acquired through smart phone cameras. We not only assess the performance of the proposed deep learning architecture on standard food data sets (e.g., our model achieves 91:91% accuracy on ETH’'s Food-101), but also demonstrate the performance of our models on our own, custom data sets comprising local cuisine, such as the Pizza-Styles data set and GCC-30. The former comprises 14 categories of pizza styles, whereas the latter contains 30 Middle Eastern dishes from the Gulf Cooperation Council members.en_US
dc.description.sectionheadersModeling, Reconstruction, and Applications
dc.description.seriesinformationSmart Tools and Apps for Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20211476
dc.identifier.isbn978-3-03868-165-6
dc.identifier.issn2617-4855
dc.identifier.pages73-83
dc.identifier.urihttps://doi.org/10.2312/stag.20211476
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20211476
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman centered computing
dc.subjectUbiquitous and mobile computing
dc.subjectComputing methodologies
dc.subjectScene understanding
dc.subjectObject recognition
dc.titleSlowDeepFood: a Food Computing Framework for Regional Gastronomyen_US
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
073-083.pdf
Size:
13.82 MB
Format:
Adobe Portable Document Format
Download
Collections
Italian Chapter Conference 2021 - Smart Tools and Apps in Graphics