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Segmentation Assisted Food Classification for Dietary Assessment

机译：细分辅助食品分类以进行膳食评估

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Accurate methods and tools to assess food and nutrient intake are essential for the association between diet and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain images of the food consumed may provide a less burdensome and more accurate method for dietary assessment. We are developing methods to identify food items using a single image acquired from the mobile device. Our goal is to automatically determine the regions in an image where a particular food is located (segmentation) and correctly identify the food type based on its features (classification or food labeling). Images of foods are segmented using Normalized Cuts based on intensity and color. Color and texture features are extracted from each segmented food region. Classification decisions for each segmented region are made using support vector machine methods. The segmentation of each food region is refined based on feedback from the output of classifier to provide more accurate estimation of the quantity of food consumed.
机译：评估食物和营养摄入量的准确方法和工具对于饮食与健康之间的关联至关重要。初步研究表明，使用带有内置摄像头的移动设备获取所食用食物的图像可能为饮食评估提供了一种更轻松，更准确的方法。我们正在开发使用从移动设备获取的单个图像来识别食品的方法。我们的目标是自动确定图像中特定食物所在的区域（细分），并根据其特征（分类或食物标签）正确识别食物类型。使用基于强度和颜色的归一化分割对食物图像进行分割。从每个分割的食物区域提取颜色和纹理特征。使用支持向量机方法对每个分割区域进行分类决策。每个食物区域的细分基于分类器输出的反馈进行细化，以提供对所食用食物数量的更准确估计。

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