一种基于属性加权朴素贝叶斯算法的OTSU图像分割方法

doi:10.3969/j.issn.0255-8297.2022.02.005

Abstract

Abstract: 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.

Key words: OTSU image segmentation method, attribute weighted naive Bayesian, optimal threshold, improved OTSU image segmentation method

CLC Number:

TP391

MA Feihu, ZENG Cong, JIN Yichen, SUN Cuiyu, CHEN Huapeng. An OTSU Image Segmentation Method Based on Attribute Weighted Naive Bayesian Algorithm[J]. Journal of Applied Sciences, 2022, 40(2): 224-232.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2022.02.005

https://www.jas.shu.edu.cn/EN/Y2022/V40/I2/224

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

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