Dietary assessment is essential to maintaining a healthy lifestyle. Automatic image-based dietary assessment is a growing field of research due to the increasing prevalence of image capturing devices (e.g. mobile phones). In this work, we estimate food energy from a single monocular image, a difficult task due to the limited hard-to-extract amount of energy information present in an image. To do so, we employ an improved encoder-decoder framework for energy estimation; the encoder transforms the image into a representation embedded with food energy information in an easier-to-extract format, which the decoder then extracts the energy information from. To implement our method, we compile a high-quality food image dataset verified by registered dietitians containing eating scene images, food-item segmentation masks, and ground truth calorie values. Our method improves upon previous caloric estimation methods by over 10\% and 30 kCal in terms of MAPE and MAE respectively.

翻译：膳食评估对于维持健康生活方式至关重要。随着图像捕捉设备（如手机）的日益普及，基于图像的自动膳食评估已成为一个不断发展的研究领域。在本研究中，我们通过单目图像估计食物能量，这是一项困难的任务，因为图像中蕴含的能量信息有限且难以提取。为此，我们采用一种改进的编码器-解码器框架进行能量估计：编码器将图像转换为嵌入食物能量信息且更易提取的表示形式，解码器则从中提取能量信息。为实施我们的方法，我们编制了一个由注册营养师验证的高质量食物图像数据集，其中包含进食场景图像、食物分割掩膜以及真实热量值。我们的方法在平均绝对百分比误差（MAPE）和平均绝对误差（MAE）上分别比先前的热量估计方法改进了超过10%和30千卡。