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Bi-level Classification Using Naive Bayes Classifier and Gaussian Line-Seeker Method on Rice Leaf Images

机译：基于朴素贝叶斯分类器和高斯寻线法的水稻叶片图像双分类

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In this study, supervised and contextual classification are combined for a bi-level model of categorizing rice leaf pixels according to three main clusters: vein, mesophyll cells and unknown (chlorophyll concentration and other parts of the leaf). Gaussian Line-Seeker Method (GLSM) performs first-level classification based on neighboring pixels. Higher- intensity pixels are identified as candidates for further classification. These candidate pixels are subjected to Naive Bayes Classifier. Hue, saturation and intensity are the independent predictors used to construct the posterior probability of class membership. This bi-level model is developed to facilitate an automated screening process for the C4 Rice Project. It is a pro ject of the International Rice Research Institute (IRRI) requiring the screening of more than 100,000 leaf samples of rice varieties and cultivars. The veins were classified with an average precision of 75.07%, 71.99% for mesophyll cells and 98.45% for the unknown cluster.
机译：在这项研究中，将监督分类和上下文分类相结合，以根据三个主要簇将稻叶像素分类的双级模型：静脉，叶肉细胞和未知簇（叶绿素浓度和叶的其他部分）。高斯线搜索器方法（GLSM）基于相邻像素执行第一级分类。较高强度的像素被标识为进一步分类的候选对象。这些候选像素经过朴素贝叶斯分类器处理。色相，饱和度和强度是用于构造班级隶属度的后验概率的独立预测因子。开发这种双层模型是为了促进C4水稻项目的自动化筛选过程。它是国际水稻研究所（IRRI）的一个项目，需要筛选100,000多个水稻品种和栽培品种的叶片样品。对静脉进行分类的平均精度为75.07％，叶肉细胞的平均精度为71.99％，未知簇的平均精度为98.45％。

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