一种粗糙不确定的图像分割方法

doi:10.11896/jsjkx.190500177

计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 72-75.doi: 10.11896/jsjkx.190500177

• 计算机图形学&多媒体 • 上一篇下一篇

一种粗糙不确定的图像分割方法

饶梦,苗夺谦,罗晟

(同济大学电子与信息工程学院上海201804)¹;
(嵌入式系统与服务计算教育部重点实验室(同济大学) 上海201804)²

收稿日期:2019-05-13 出版日期:2020-02-15 发布日期:2020-03-18
通讯作者: 苗夺谦(dqmiao@tongji.edu.cn)
基金资助:
国家重点研发计划项目(213);国家自然科学基金(61673301,61563016);公安部重大专项(20170004)

Rough Uncertain Image Segmentation Method

RAO Meng,MIAO Duo-qian,LUO Sheng

(Department of Electronics and Information Engineering,Tongji University,Shanghai 201804,China)¹;
(Key Lab of Embedded System and Service Computing(Tongji University),Ministry of Education,Shanghai 201804,China)²

Received:2019-05-13 Online:2020-02-15 Published:2020-03-18
About author:RAO Meng,born in 1994,postgraduate.Her main research interests include rough sets and machine learning;MIAO Duo-qian,born in 1964,Ph.D,professor,Ph.D supervisor,is member of China Computer Federation.His main research interests include rough sets,granular computing and machine learning.
Supported by:
This work was supported by the National Key R&D Program of China (213), National Natural Science Foundation of China (61673301, 61563016) and Major Project of Ministry of Public Security (20170004).

摘要/Abstract

摘要： 图像分割是计算机视觉领域的一个基础问题,涉及图像检索、物体检测、物体识别、行人跟踪等众多后续任务。目前已有大量研究成果,有基于阈值、聚类、区域生长的传统方法,也有基于神经网络的流行算法。由于图像区域边界的不确定性问题,现有算法并没有很好地解决图像部分区域渐变导致的边界模糊问题。粒计算是解决复杂问题的有效工具之一,在不确定的、模糊的问题上取得了良好的效果。针对现有图像分割算法在不确定性问题上的局限性,基于粒计算思想,提出了一种粗糙不确定性的图像分割方法。该算法在K均值算法的基础上,结合邻域粗糙集模型,先对类别边界区域的像素点进行粒化,运用邻域关系矩阵,得到各类别对各粒化像素点的包含度,从而对边界区域类别模糊的像素点进行重新划分,优化了图像分割的结果。在Matlab2019编程环境中,实验选取了BSDS500数据集中的一张马术训练图片和一张建筑物图片来测试算法性能。实验先对彩色图像进行灰度处理,用K均值算法对图像进行初步分割,再设置邻域因子值,依据边界像素点邻域信息重新划分边界点。对比K均值算法的分割结果可知,所提算法取得了更佳的效果。实验结果表明,该方法在粗糙度这一评价标准上优于K均值算法,可以有效降低图像区域边界的模糊性,实现灰度边界模糊的图像渐变区域的分割。

关键词: K均值, 边界模糊, 粒计算, 邻域粗糙集, 图像分割

Abstract: Image segmentation is a fundamental problem in the field of computer vision,involving image retrieval,object detection,object recognition,pedestrian tracking and many other follow-up tasks.At present,there are a lot of research results,including traditional methods based on threshold,clustering and region growing,and popular algorithms based on neural networks.Due to the boundary uncertainty of the image region,the existing algorithms are not suitable for solving the problem of partial gradation of the image.Granular computing is one of the effective tools for solving complex problems,and has achieved good results on uncertain and fuzzy problems.Aiming at the limitation of the existing image segmentation algorithms in the uncertainty problem,based on the idea of granular computing,a rough uncertain image segmentation method was proposed in this paper.Based on the K-means algorithm and the neighborhood rough set model,this algorithm granulates the pixel points at the edge of the cluster,and uses the neighborhood matrix to calculate the inclusion degree of the clusters for the granulated pixels.Finally,the optimization of class clustering of edge pixels is realized.In the Matlab2019 programming environment,the experiment selected an equestrian training picture and a picture of a building in the BSDS500 data set to test the algorithm.Firstly,the color image is processed by grading,and the K-means algorithm is used to segment the image.Then,the value of the neighborhood factor is set,and the edge point is re-divided according to the neighborhood information of the edge pixel.Compared with the K-means algorithm,this algorithm can achieve better results.The experimental results show that the proposed method outperforms the K-means algorithm in the evaluation of roughness,which can effectively reduce the blurring of the image region boundary and realize the segmentation of the image gradient region with gray boundary blur.

Key words: Boundary blur, Granular computing, Image segmentation, K-means, Neighborhood rough set

中图分类号:

TP391

饶梦,苗夺谦,罗晟. 一种粗糙不确定的图像分割方法[J]. 计算机科学, 2020, 47(2): 72-75. https://doi.org/10.11896/jsjkx.190500177

RAO Meng,MIAO Duo-qian,LUO Sheng. Rough Uncertain Image Segmentation Method[J]. Computer Science, 2020, 47(2): 72-75. https://doi.org/10.11896/jsjkx.190500177

参考文献

[1]FELZENSZWALB P F,HUTTENLOCHER D P.Efficient Graph-Based Image Segmentation[J].International Journal of Computer Vision,2004,59(2):167-181.
[2]ZADEH L A.Toward a Theory of Fuzzy Information Granulation and its Centrality in Human Reasoning and Fuzzy Logic[J].Fuzzy Sets & Systems,1997,90(90):111-127.
[3]苗夺谦,王国胤,刘清,等.粒计算:过去、现在与展望[M].北京:科学出版社,2007.
[4]PAWLAK Z.Rough Sets[J].International Journal of Parallel Programming,1982,11(5):341-356.
[5]苗夺谦,李道国.粗糙集理论、算法与应用[M].北京:清华大学出版社,2008.
[6]HU Q H,YU D R,XIE Z X.Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation[J].Journal of Software,2008,19(3):640-649.
[7]LI F,MIAO D Q,LIU C H,et al.Image Segmentation Algorithm Based on the Decision-Theoretic Rough Set Model[J].CAAI Transactions on Intelligent Systems,2004,9(2):143-147.
[8]ZHANG Y C,GUO H.Level Set Image Segmentation Based on Rough Set and New Energy Formula[J].Acta Automatica Sinica,2015,41(11):1913-1925.
[9]VENKATESWARAREDDY E,REDDY E.Image Segmentation Using Rough Set Based Fuzzy K-means Algorithm[J].International Journal of Computer Applications,2014,74(14):36-40.
[10]JI Z,HUANG Y,XIA Y,et al.A Robust Modified Gaussian Mixture Model with Rough Set for Image Segmentation[J].Neurocomputing,2017,266:550-565.
[11]MOHABE A,RAY A K.Fusion of Rough Set Theoretic Approximations and FCM for Color Image Segmentation[C]∥Conference:Systems,Man,and Cybernetics.2000,2:1529-1534.
[12]PAL S K,SHANKAR B U,MITRA P.Granular Computing,Rough Entropy and Object Extraction[J].Pattern Recognition Letters,2005,26(16):2509-2517.
[13]MALYSZKO D,STEPANIUK J.Adaptive Multilevel Rough Entropy Evolutionary Thresholding[J].Information Sciences,2009,180(7):1138-1158.
[14]SRIKUMAR S,WAGH M,NANDA P K.Adaptive Windowing and Granular Computing Based Image Segmentation[C]∥International Conference on Energy.IEEE,2011.
[15]MACQUEEN J B.Some Methods for Classification and Analysis of Multivariate Observations,Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability[C]∥Conference:Proceedings of 5th Symposium on Mathematical Statistics and Probability.1967:281-297.
[16]LIN T Y.Granular Computing on Binary Relations in Data Mining and Neighborhood Systems[C]∥Proc.of the Rough Sets in Knowledge Discovery.Physica-Verlag,1998:107-121.
[17]WU W Z,ZHANG W X.Neighborhood Operator Systems and Approximations[J].Information Sciences,2002,144(1／2／3／4):201-217.
[18]ARBELAEZ P,MAIRE M,FOWLKES C,et al.Contour Detection and Hierarchical Image Segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(5):898-916.

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一种粗糙不确定的图像分割方法

Rough Uncertain Image Segmentation Method

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