A Central Asian Food Dataset for Personalized Dietary Interventions, Extended Abstract

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ID: 282965
2023
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Abstract
Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to create a reliable dataset of regional foods that is easily accessible to both public consumers and researchers. To the best of our knowledge, this is the first work on creating a Central Asian Food Dataset (CAFD). The final dataset contains 42 food categories and over 16,000 images of national dishes unique to this region. We achieved a classification accuracy of 88.70\% (42 classes) on the CAFD using the ResNet152 neural network model. The food recognition models trained on the CAFD demonstrate computer vision's effectiveness and high accuracy for dietary assessment.
Reference Key
chan2023a Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aknur Karabay; Arman Bolatov; Huseyin Atakan Varol; Mei-Yen Chan
Journal arXiv
Year 2023
DOI
DOI not found
URL
http://arxiv.org/abs/2305.07257v1
Keywords
cs.lg cs.cv

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