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.

References

[1] 李林国.基于群智优化的多阈值图像分割算法研究[D].南京:南京邮电大学, 2017.
[2] Min T H, Park R H. Eyelid and eyelash detection method in the normalized iris image using the parabolic Hough model and Otsu's thresholding method[J]. Pattern Recognition Letters, 2009, 30(12):1138-1143.
[3] 曹爽.基于OTSU算法的图像阈值分割技术[D].太原:太原理工大学, 2018.
[4] 易三莉,张桂芳,贺建峰,等.基于最大类间方差的最大熵图像分割[J].计算机工程与科学, 2018, 40(20):1874-1881. Yi S L, Zhang G F, He J F, et al. Maximum entropy image segmentation based on maximum interclass variance[J]. Computer Engineering&Science, 2018, 40(10):1874-1881.(in Chinese)
[5] 张群会,李贵敏,蔺宝华,等.基于最大熵-方差模型的图像分割方法[J].计算机技术与发展, 2011, 21(6):43-46. Zhang Q H, Li G M, Lin B H, et al. Threshold image segmentation based on maximum entropy-variance model[J]. Computer Technology and Development, 2011, 21(6):43-46.(in Chinese)
[6] 魏晶茹,马瑜,夏瑞,等.基于分数阶粒子群的OTSU图像分割算法[J].计算机工程与设计, 2017, 38(12):3284-3290. Wei J R, Ma Y, Xia R, et al. Image segmentation algorithm based on OTSU optimized by fractional-order particle swarm optimization[J]. Computer Engineering and Design, 2017, 38(12):3284-3290.(in Chinese)
[7] 罗钧,刘建强,庞亚男.基于邻域搜索JADE的二维OTSU多阈值图像分割[J].系统工程与电子技术, 2020, 42(10):2164-2171. Luo J, Liu J Q, Pang Y N. Multi-threshold image segmentation of 2D OTSU based on neighborhood search JADE[J]. Systems Engineering and Electronics, 2020, 42(10):2164-2171.(in Chinese)
[8] 保玉俊,周莉莉,段鹏.一种基于遗传算法的加权朴素贝叶斯分类算法[J].云南民族大学学报(自然科学版), 2018, 27(6):525-529. Bao Y J, Zhou L L, Duan P. A weighted naive Bayes classification algorithm based on a genetic algorithm[J]. Journal of Yunnan Minzu University (Natural Sciences Edition), 2018, 27(6):525-529.(in Chinese)
[9] 郭勋诚.朴素贝叶斯分类算法应用研究[J].通讯世界, 2019, 26(1):241-242. Guo X C. Application research of naive Bayesian classification algorithm[J]. Telecom World, 2019, 26(1):241-242.(in Chinese)
[10] 张弘,高威.基于局部空间信息的阈值分割算法[J].计算机与数字工程, 2018, 46(2):251-255, 325. Zhang H, Gao W. Thresholding segmentation algorithm based on local spatial information[J]. Computer&Digital Engineering, 2018, 46(2):251-255, 325.(in Chinese)
[11] 饶丽丽,刘雄辉,张东站.基于特征相关的改进加权朴素贝叶斯分类算法[J].厦门大学学报(自然科学版), 2012, 51(4):682-685. Rao L L, Liu X H, Zhang D Z. An improved weighted naive Bayes classification algorithm using feature correlation[J]. Journal of Xiamen University (Natural Science), 2012, 51(4):682-685.(in Chinese)
[12] 邹晓辉.朴素贝叶斯算法在文本分类中的应用[J].数字技术与应用, 2017(12):132-133. Zou X H. Application of naive Bayesian algorithm in text classification[J]. Digital Technology and Application, 2017(12):132-133.(in Chinese)
[13] 徐全飞.海洋遥感图像亚像素配准算法关键技术研究[D].北京:中国科学院大学(中国科学院上海技术物理研究所), 2017.
[14] 代大攀.基于后验概率和流形正则化的半监督分类方法研究[D].武汉:华中科技大学, 2012.
[15] 孙秀亮.基于属性加权的选择性朴素贝叶斯分类研究[D].哈尔滨:哈尔滨工程大学, 2013.
[16] Fan H D, Xie F Y, Li Y, et al. Automatic segmentation of dermoscopy images using saliency combined with OTSU threshold[J]. Computers in Biology and Medicine, 2017, 85:75-85.
[17] Yang P, Song W, Zhao X B, et al. An improved OTSU threshold segmentation algorithm[J]. International Journal of Computational Science and Engineering, 2020, 22(1):146.
[18] 孙博,訾红梅.贝叶斯估计的图像分割技术[J].价值工程, 2012, 31(17):151-152. Sun B, Zi H M. The research of image segmentation based on Bayesian estimation[J]. Value Engineering, 2012, 31(17):151-152.(in Chinese)

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

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