Computer Science ›› 2020, Vol. 47 ›› Issue (2): 72-75.doi: 10.11896/jsjkx.190500177

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Rough Uncertain Image Segmentation Method

RAO Meng,MIAO Duo-qian,LUO Sheng   

  1. (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).

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

CLC Number: 

  • TP391
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