Multi-classification of pizza using computer vision and support vector machine
Multi-classification of pizza using computer vision and support vector machine
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DOI：
10.1016/j.jfoodeng.2007.10.001
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发表时间：
2008-05
影响因子：
5.5
通讯作者：
C. Du;Da‐Wen Sun
中科院分区：
文献类型：
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作者：
C. Du;Da‐Wen Sun
比萨饼底、酱料涂抹和浇头的分类由于其主观性和不一致性而对人为错误高度敏感。结合机器学习的图像处理技术提供了一种客观和一致的方法来完成这项任务。支持向量机（SVM）是一种结合使用多个二进制分类器的最先进的学习算法，用于比萨饼底、酱料涂和浇头的多分类。以所选择的特征作为输入，一对一和有向无环图（DAG）方法分别实现了89.17%和88.33%的多分类准确率为比萨饼基地，比萨酱料蔓延，比萨饼浇头分别为80.83%和80.00%。结果表明，开发的计算机视觉系统有很大的潜力，以协助自动多分类比萨饼基地，酱料蔓延，和浇头。
The classification of pizza base, sauce spread and topping is highly sensitive to human error for its subjective and inconsistent nature. Image processing techniques combined with machine learning provide an objective and consistent way to accomplish this task. By using a combination of several binary classifiers, support vector machine (SVM) is a state-of-the-art learning algorithm for multi-classification of pizza base, sauce spread, and topping. With the selected features as input, the one-versus-one and directed acyclic graph (DAG) methods achieved 89.17% and 88.33% multi-classification accuracy respectively for pizza base, both 87.5% for pizza sauce spread, and 80.83% and 80.00%, respectively for pizza topping. The results showed that the computer vision systems developed had a great potential to assist in the automatic multi-classification of pizza base, sauce spread, and topping.