Liu Chengxu, Sheng Guorui, Min Weiqing, Wu Xiaojun, Jiang Shuqiang
School of Information and Electrical Engineering, Ludong University, Yantai 264025, China.
The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
Foods. 2025 Aug 28;14(17):3016. doi: 10.3390/foods14173016.
Precisely identifying and delineating food regions automatically from images, a task known as food image segmentation, is crucial for enabling applications in food science such as automated dietary logging, accurate nutritional analysis, and food safety monitoring. However, accurately segmenting food images, particularly delineating food edges with precision, remains challenging due to the wide variety and diverse forms of food items, frequent inter-food occlusion, and ambiguous boundaries between food and backgrounds or containers. To overcome these challenges, we proposed a novel method called the Multi-view Edge Attention Network (MVEANet), which focuses on enhancing the fine-grained segmentation of food edges. The core idea behind this method is to integrate information obtained from observing food from different perspectives to achieve a more comprehensive understanding of its shape and specifically to strengthen the processing capability for food contour details. Rigorous testing on two large public food image datasets, FoodSeg103 and UEC-FoodPIX Complete, demonstrates that MVEANet surpasses existing state-of-the-art methods in segmentation accuracy, performing exceptionally well in depicting clear and precise food boundaries. This work provides the field of food science with a more accurate and reliable tool for automated food image segmentation, offering strong technical support for the development of more intelligent dietary assessment, nutritional research, and health management systems.
从图像中自动精确识别和勾勒食物区域，即所谓的食物图像分割任务，对于实现食品科学中的应用至关重要，如自动饮食记录、准确的营养分析和食品安全监测。然而，由于食物种类繁多、形态各异，食物之间频繁遮挡，以及食物与背景或容器之间界限模糊，准确分割食物图像，尤其是精确勾勒食物边缘，仍然具有挑战性。为了克服这些挑战，我们提出了一种名为多视图边缘注意力网络（MVEANet）的新方法，该方法专注于增强食物边缘的细粒度分割。该方法背后的核心思想是整合从不同视角观察食物所获得的信息，以更全面地了解其形状，特别是增强对食物轮廓细节的处理能力。在两个大型公共食物图像数据集FoodSeg103和UEC - FoodPIX Complete上进行的严格测试表明，MVEANet在分割精度方面超越了现有的最先进方法，在描绘清晰精确的食物边界方面表现出色。这项工作为食品科学领域提供了一种更准确可靠的自动食物图像分割工具，为开发更智能的饮食评估、营养研究和健康管理系统提供了有力的技术支持。
