| 基于图像处理和支持向量机的初烤烟叶颜色特征区域分类 |
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| 引用本文： | 潘治利,祁萌,魏春阳,李锋,张仕祥,王建伟,过伟民,艾志录. 基于图像处理和支持向量机的初烤烟叶颜色特征区域分类[J]. 作物学报, 2012, 38(2): 374-379. DOI: 10.3724/SP.J.1006.2012.00374 |
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| 作者姓名： | 潘治利 祁萌 魏春阳 李锋 张仕祥 王建伟 过伟民 艾志录 |
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| 作者单位： | 1 河南农业大学食品科学技术学院, 河南郑州450002; 2 华北水利水电学院, 河南郑州 450008; 3中国烟草总公司郑州烟草研究院, 河南郑州 450001 |
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| 基金项目： | 河南省重点学科青年骨干教师建设项目(豫教高[2008]169号)资助 |
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| 摘 要： | 颜色是烤烟烟叶品质的重要外在指标之一, 在生产中, 同类颜色烟叶在不同产地却往往存在着较大的差异。采用区域生长方法对烟叶图像进行分割预处理, 然后提取烟叶的颜色特征, 再运用一种新的机器学习算法—支持向量机分类方法对我国烟叶颜色特征进行区域分类。结果发现在小样本情况下, 采用径向基函数作为支持向量模型的核函数, 并确定了适当的模型参数, 所建立模型对烟叶颜色区域特征的回判识别率达100%, 预测识别率达86.67%。支持向量机对典型产地烟叶颜色的分类识别具有良好的应用性能。
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| 关 键 词： | 烟叶 支持向量机 颜色 分类 |
| 收稿时间： | 2011-06-02 |
Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine |
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| PAN Zhi-Li,QI Meng,WEI Chun-Yang,LI Feng,ZHANG Shi-Xiang,WANG Jian-Wei,GUO Wei-Min,and AI Zhi-Lu. Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine[J]. Acta Agronomica Sinica, 2012, 38(2): 374-379. DOI: 10.3724/SP.J.1006.2012.00374 |
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| Authors: | PAN Zhi-Li QI Meng WEI Chun-Yang LI Feng ZHANG Shi-Xiang WANG Jian-Wei GUO Wei-Min and AI Zhi-Lu |
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| Affiliation: | 1.College of Food Science and Technology, Henan Agricultural University, Zhengzhou 450002, China;2.North China University of Water Conservancy and Electric Power, Zhengzhou 450008, China;3.Zhengzhou Tobacco Research Institute of China National Tobacco Corporation, Zhengzhou 450001, China |
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| Abstract: | Leaf color is an important indicator of flue-cured tobacco quality;however,there is a big difference among similar color tobacco leaves from different areas.Color features of tobacco leaves were obtained by using Region Growing Method in the pre-processing,and then made region classification by using Support Vector Machine(SVM).The results showed that in the case of small samples,radial basis function was the kernel function of the SVM,and the appropriate model parameters were determined.The classification accuracies for training set and test set of the SVM model were 100% and 86.67%,showing that SVM has a perfect performance in color region classification of tobacco leaves. |
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| Keywords: | Tobacco leaf Support vector machine Color Classification |
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