IEEE J Biomed Health Inform. 2017 May;21(3):588-598. doi: 10.1109/JBHI.2016.2636441. Epub 2016 Dec 7.
We propose a new dataset for the evaluation of food recognition algorithms that can be used in dietary monitoring applications. Each image depicts a real canteen tray with dishes and foods arranged in different ways. Each tray contains multiple instances of food classes. The dataset contains 1027 canteen trays for a total of 3616 food instances belonging to 73 food classes. The food on the tray images has been manually segmented using carefully drawn polygonal boundaries. We have benchmarked the dataset by designing an automatic tray analysis pipeline that takes a tray image as input, finds the regions of interest, and predicts for each region the corresponding food class. We have experimented with three different classification strategies using also several visual descriptors. We achieve about 79% of food and tray recognition accuracy using convolutional-neural-networks-based features. The dataset, as well as the benchmark framework, are available to the research community.
我们提出了一个新的数据集，用于评估可用于饮食监测应用的食物识别算法。每个图像描绘了一个真实的食堂托盘，其中的菜肴和食物以不同的方式排列。每个托盘包含多个食物类别的实例。该数据集包含 1027 个食堂托盘，共计 3616 个食物实例，属于 73 个食物类别。使用仔细绘制的多边形边界，手动对托盘图像中的食物进行了分割。我们通过设计一个自动托盘分析管道来对数据集进行基准测试，该管道以托盘图像作为输入，找到感兴趣的区域，并为每个区域预测相应的食物类别。我们使用三种不同的分类策略进行了实验，还使用了几种视觉描述符。我们使用基于卷积神经网络的特征实现了约 79%的食物和托盘识别准确率。数据集以及基准框架可供研究社区使用。
