Jožef Stefan International Postgraduate School;
Computer Systems Department, Jožef Stefan Institute.
J Vis Exp. 2021 Mar 13(169). doi: 10.3791/61906.
Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a much simpler way, such as by taking an image with a smartphone camera. In this article, we will focus on such image-based approaches to dietary assessment. For the food image recognition problem, deep neural networks have achieved the state of the art in recent years, and we present our work in this field. In particular, we first describe the method for food and beverage image recognition using a deep neural network architecture, called NutriNet. This method, like most research done in the early days of deep learning-based food image recognition, is limited to one output per image, and therefore unsuitable for images with multiple food or beverage items. That is why approaches that perform food image segmentation are considerably more robust, as they are able to identify any number of food or beverage items in the image. We therefore also present two methods for food image segmentation - one is based on fully convolutional networks (FCNs), and the other on deep residual networks (ResNet).
由于手动饮食评估方法存在问题和成本，因此需要自动化解决方案来简化、加快工作并提高其质量。如今，自动化解决方案能够以更简单的方式记录个人的饮食摄入情况，例如通过智能手机摄像头拍摄图像。在本文中，我们将重点介绍基于图像的饮食评估方法。对于食物图像识别问题，深度神经网络近年来已经达到了技术的前沿，我们展示了在这一领域的工作。具体来说，我们首先描述了一种使用深度神经网络架构（称为 NutriNet）进行食物和饮料图像识别的方法。这种方法与深度学习在食物图像识别早期阶段所做的大多数研究一样，仅限于每个图像一个输出，因此不适合具有多个食物或饮料项目的图像。这就是为什么执行食物图像分割的方法更加强大，因为它们能够识别图像中的任意数量的食物或饮料项目。因此，我们还展示了两种食物图像分割方法 - 一种基于全卷积网络（FCN），另一种基于深度残差网络（ResNet）。
