In order to improve the segmentation accuracy of plant lesion images, multi-channels segmentation algorithm of plant disease image was proposed based on linear discriminant analysis (LDA) method’s mapping and K-means’ clustering. Firstly, six color channels from RGB model and HSV model were obtained, and six channels of all pixels were laid out to six columns. Then one of these channels was regarded as label and the others were regarded as sample features. These data were grouped for linear discrimination analysis, and the mapping values of the other five channels were applied to the eigen vector space according to the first three big eigen values. Secondly, the mapping value was used as the input data for K-means and the points with minimum and maximum pixel values were used as the initial cluster center, which overcame the randomness for selecting the initial cluster center in K-means. And the segmented pixels were changed into background and foreground, so that the proposed segmentation method became the clustering of two classes for background and foreground. Finally, the experimental result showed that the segmentation effect of the proposed LDA mapping-based method is better than those of K-means, ExR and CIVE methods.

中文翻译：

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基于LDA的多通道映射图像分割方法

为了提高植物病害图像的分割精度，提出了基于线性判别分析(LDA)方法的映射和K-means聚类的植物病害图像多通道分割算法。首先，从 RGB 模型和 HSV 模型中得到六个颜色通道，并将所有像素的六个通道布局为六列。然后将这些通道中的一个视为标签，将其他通道视为样本特征。将这些数据分组进行线性判别分析，并根据前三个大特征值将其他五个通道的映射值应用于特征向量空间。其次，将映射值作为K-means的输入数据，将像素值最小和最大的点作为初始聚类中心，它克服了在 K-means 中选择初始聚类中心的随机性。并将分割后的像素变为背景和前景，使得所提出的分割方法成为背景和前景两类的聚类。最后，实验结果表明，所提出的基于 LDA 映射的方法的分割效果优于 K-means、ExR 和 CIVE 方法。