Zhang Weiyu, Yu Qian, Siddiquie Behjat, Divakaran Ajay, Sawhney Harpreet
SRI International, Princeton, NJ, USA.
SRI International, Princeton, NJ, USA
J Diabetes Sci Technol. 2015 May;9(3):525-33. doi: 10.1177/1932296815582222. Epub 2015 Apr 21.
We present snap-n-eat, a mobile food recognition system. The system can recognize food and estimate the calorific and nutrition content of foods automatically without any user intervention. To identify food items, the user simply snaps a photo of the food plate. The system detects the salient region, crops its image, and subtracts the background accordingly. Hierarchical segmentation is performed to segment the image into regions. We then extract features at different locations and scales and classify these regions into different kinds of foods using a linear support vector machine classifier. In addition, the system determines the portion size which is then used to estimate the calorific and nutrition content of the food present on the plate. Previous approaches have mostly worked with either images captured in a lab setting, or they require additional user input (eg, user crop bounding boxes). Our system achieves automatic food detection and recognition in real-life settings containing cluttered backgrounds. When multiple food items appear in an image, our system can identify them and estimate their portion size simultaneously. We implemented this system as both an Android smartphone application and as a web service. In our experiments, we have achieved above 85% accuracy when detecting 15 different kinds of foods.
我们展示了“快拍即食”，一种移动食品识别系统。该系统能够自动识别食物，并在无需任何用户干预的情况下估算食物的热量和营养成分。为了识别食物项目，用户只需拍摄餐盘的照片。系统检测显著区域，裁剪其图像，并相应地减去背景。进行分层分割以将图像分割成区域。然后我们在不同位置和尺度上提取特征，并使用线性支持向量机分类器将这些区域分类为不同种类的食物。此外，系统确定食物分量大小，然后用于估算餐盘上食物的热量和营养成分。以前的方法大多适用于在实验室环境中拍摄的图像，或者需要额外的用户输入（例如，用户裁剪边界框）。我们的系统在包含杂乱背景的现实生活场景中实现了自动食物检测和识别。当图像中出现多个食物项目时，我们的系统可以识别它们并同时估算它们的分量大小。我们将此系统实现为一个安卓智能手机应用程序和一个网络服务。在我们的实验中，检测15种不同食物时，我们达到了85%以上的准确率。
