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Human Visual System vs Convolution Neural Networks in food recognition task: An empirical comparison
Computer Vision and Image Understanding ( IF 4.5 ) Pub Date : 2019-11-30 , DOI: 10.1016/j.cviu.2019.102878
Pedro Furtado , Manuel Caldeira , Pedro Martins

Automated food recognition from food plate is useful for smartphone-based applications promoting healthy lifestyles and for automated carbohydrate counting, e.g. targeted at type I diabetic patients, but the variation of appearance of food items makes it a difficult task. Convolution Neural Networks (CNNs) raised to prominence in recent years, and they will enable those applications if they are able to match HVS accuracy at least in meal classification. In this work we run an experimental comparison of accuracy between CNNs and HVS based on a simple meal recognition task. We set up a survey for humans with two phases, training and testing, and also give the food dataset to state-of-the-art CNNs. The results, considering some relevant variations in the setup, allow us to reach conclusions regarding the comparison, characteristics and limitations of CNNs, which are relevant for future improvements.



中文翻译:

人类视觉系统与卷积神经网络在食品识别任务中的实证比较

从食物盘自动识别食物可用于基于智能手机的应用程序,以促进健康的生活方式和自动碳水化合物计数(例如,针对I型糖尿病患者),但是食物外观的变化使其成为一项艰巨的任务。卷积神经网络(CNN)近年来发展迅速,如果至少在膳食分类方面能够与HVS精度相匹配,它们将使这些应用成为可能。在这项工作中,我们基于简单的膳食识别任务对CNN和HVS之间的准确性进行了实验比较。我们通过两个阶段(培训和测试)对人类进行了调查,并将食物数据集提供给了最新的CNN。考虑到设置中的一些相关变化,结果使我们能够得出有关比较的结论,

更新日期:2020-01-04
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