一种基于属性加权朴素贝叶斯算法的OTSU图像分割方法

为了增强图像分割技术的准确性并优化图像分割技术的细节分割效果，提出了一种基于属性加权朴素贝叶斯算法的OTSU图像分割方法。将OTSU算法中依据图像灰度特征选取的图像中的前景和背景通过属性加权朴素贝叶斯算法进行分类处理，计算图像中前景和背景的概率，训练该模型以获得最佳阈值进行图像分割处理，优化图像分割的效果。利用无人机航拍采集的图像数据进行实验，结果显示基于属性加权朴素贝叶斯算法的OTSU图像分割方法优化了图像的分割效果，较完整地展示了分割后的图像细节，具有较好的应用价值。

An OTSU Image Segmentation Method Based on Attribute Weighted Naive Bayesian Algorithm

In order to enhance the accuracy of image segmentation and optimize the detail segmentation effect of image segmentation, an improved OTSU image segmentation method based on attribute weighted naive Bayesian algorithm is proposed. The foreground and background of an image are selected according to the grayscale characteristics of the image as using OTSU algorithm, and then classified by using the attribute Weighted Naive Bayesian algorithm. Thus, the probability of the foreground and background of the image is calculated. By training this model to obtain the optimal threshold for the image segmentation process, the optimized effect of image segmentation can be obtained. Experiment with image data collected by drone aerial photography is conducted, and results show that the image segmentation of OTSU based on the attribute weighted naive Bayesian algorithm optimizes the image segmentation effect and shows much finer details of the image after segmentation, promising a prospective application value.