A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model
A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model
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DOI：
10.1109/jbhi.2014.2308928
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发表时间：
2014-07-01
影响因子：
7.7
通讯作者：
Mougiakakou, Stavroula G.
中科院分区：
文献类型：
--
作者：
Anthimopoulos, Marios M.;Gianola, Lauro;Mougiakakou, Stavroula G.
基于计算机视觉的食物识别可用于估计糖尿病患者膳食中的碳水化合物含量。本研究提出一种基于特征袋模型的自动食品识别方法。为了识别和优化BoF架构中涉及的最佳性能组件以及估计相应的参数，进行了广泛的技术调查。为了设计和评估原型系统，创建了一个包含近5000个食物图像的可视化数据集，并将其组织成11个类。优化后的系统计算密集的局部特征，使用HSV颜色空间上的尺度不变的特征变换，建立一个视觉字典的10000视觉词使用分层k-means聚类，最后分类的食物图像与线性支持向量机分类器。该系统实现了78%的分类精度的顺序，从而证明了所提出的方法在一个非常具有挑战性的图像数据集的可行性。
Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes amethodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.