[1]吴其平,吴成茂.一种快速鲁棒核空间图形模糊聚类分割算法[J].智能系统学报,2019,14(04):804-811.[doi:10.11992/tis.201806045]
 WU Qiping,WU Chengmao.A fast and robust clustering segmentation algorithm for kernel space graphics[J].CAAI Transactions on Intelligent Systems,2019,14(04):804-811.[doi:10.11992/tis.201806045]
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一种快速鲁棒核空间图形模糊聚类分割算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年04期
页码:
804-811
栏目:
出版日期:
2019-07-02

文章信息/Info

Title:
A fast and robust clustering segmentation algorithm for kernel space graphics
作者:
吴其平 吴成茂
西安邮电大学 电子工程学院, 陕西 西安 710121
Author(s):
WU Qiping WU Chengmao
School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
关键词:
图像分割图形模糊聚类核函数线性加权和图像邻域滤波二维直方图聚类有效性鲁棒性
Keywords:
image segmentationgraph fuzzy clusteringkernel functionlinear weighted imageneighborhood filteringtwo-dimensional histogramclustering validityrobustness
分类号:
TP391.41
DOI:
10.11992/tis.201806045
摘要:
针对现有鲁棒图形模糊聚类算法难以满足强噪声干扰下大幅面图像快速分割的需要,提出一种快速鲁棒核空间图形模糊聚类分割算法。该算法将欧氏空间样本通过核函数映射至高维空间;采用待分割图像中像素邻域的灰度和空间等信息构建线性加权滤波图像,对其进行鲁棒核空间图形模糊聚类;并引入当前聚类像素与其邻域像素均值所对应的二维直方图信息,获得鲁棒核空间图形模糊聚类快速迭代表达式。对大幅面图像添加高斯和椒盐噪声进行分割测试,实验结果表明:本文算法相比基于图形模糊聚类等分割算法的分割性能、抗噪鲁棒性和实时性有了显著提高。
Abstract:
Addressing the existing problem of difficulty in realizing fast segmentation of large-scale images under strong noise interference, a fast robust kernel space graph fuzzy clustering segmentation algorithm is proposed. This algorithm first mapped the samples in European Space to the high dimensional feature space through the kernel function; subsequently, it constructed the linear weighted filtering image using the gray scale and spatial information of the pixel neighborhood in the image to be segmented and carried out the robust kernel space pattern fuzzy clustering on the image. The fast iterative expression of robust kernel space graph fuzzy clustering was obtained by introducing the two-dimensional histogram information corresponding to the mean value of the current clustering pixel and its neighboring pixels. Experimental test results of large size images interrupted by Gaussian and salt-and-pepper noise show that the segmentation, robustness, and real-time performance of the proposed segmentation algorithm have improved more significantly than those of the picture-based fuzzy clustering, and other fuzzy clustering segmentation algorithms.

参考文献/References:

[1] SASIBALA P, SUDHA VANI D G. Image segmentation techniques:a review[J]. International journal of advanced research in electronics and communication engineering, 2016, 5(6):1844-1851.
[2] BLOCH I. Fuzzy sets for image processing and understanding[J]. Fuzzy sets and systems, 2015, 281:280-291.
[3] GOSAIN A, DAHIYA S. Performance analysis of various fuzzy clustering algorithms:a review[J]. Procedia computer science, 2016, 79:100-111.
[4] CHAIRA T. A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images[J]. Applied soft computing, 2011, 11(2):1711-1717.
[5] KAUR P. Intuitionistic fuzzy sets based credibilistic fuzzy C-means clustering for medical image segmentation[J]. International journal of information technology, 2017, 9(4):345-351.
[6] SON L H. DPFCM:a novel distributed picture fuzzy clustering method on picture fuzzy sets[J]. Expert systems with applications, 2015, 42(1):51-66.
[7] THONG P H, SON L H. Picture fuzzy clustering:a new computational intelligence method[J]. Soft computing, 2016, 20(9):3549-3562.
[8] ARUNA KUMAR S V, HARISH B S, MANJUNATH ARADHYA V N. A picture fuzzy clustering approach for brain tumor segmentation[C]//Proceedings of 2016 Second International Conference on Cognitive Computing and Information Processing. Mysuru, India, 2016:1-6.
[9] SON L H, THONG P H. Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences[J]. Applied intelligence, 2017, 46(1):1-15.
[10] 吴成茂, 吴其平. 一种基于改进PFCM的鲁棒图像分割算法[J]. 西安邮电大学学报, 2017, 22(5):37-43 WU Chengmao, WU Qiping. A robust image segmentation algorithm based on the improved picture fuzzy clustering method on picture fuzzy sets[J]. Journal of Xi’an University of Posts and Telecommunications, 2017, 22(5):37-43
[11] 范九伦. 模糊聚类新算法与聚类有效性问题研究[D]. 西安:西安电子科技大学, 1998. FAN Jiulun. Studies on new fuzzy clustering algorithms and clustering validity problems[D]. Xi’an:Xidian University, 1998.
[12] DE CARVALHO F D A T, SIMÕES E C, SANTANA L V C, et al. Gaussian kernel c-means hard clustering algorithms with automated computation of the width hyper-parameters[J]. Pattern recognition, 2018, 79:370-386.
[13] CAI Weiling, CHEN Songcan, ZHANG Daoqiang. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J]. Pattern recognition, 2007, 40(3):825-838.
[14] NONGMEIKAPAM K, KUMAR W K, SINGH A D. Fast and automatically adjustable GRBF kernel based fuzzy C-means for cluster-wise coloured feature extraction and segmentation of MR images[J]. IET image processing, 2018, 12(4):513-524.
[15] 丁震, 胡钟山, 杨静宇, 等. FCM算法用于灰度图象分割的研究[J]. 电子学报, 1997, 25(5):39-43 DING Zhen, HU Zhongshan, YANG Jingyu, et al. FCM algorithm for the research of intensity image segmentation[J]. Acta electronica sinica, 1997, 25(5):39-43
[16] 刘健庄. 基于二维直方图的图象模糊聚类分割方法[J]. 电子学报, 1992, 20(9):40-46 LIU Jianzhuang. A fuzzy clustering method of image segmentation based on two-dimensional histogram[J]. Acta electronica sinica, 1992, 20(9):40-46
[17] TIAN Muling, YANG Jieming. Froth image segmentation of coal flotation using weighted fuzzy C-mean clustering by two-dimensional histogram[J]. International journal of advancements in computing technology, 2013, 5(7):1203-1210.
[18] PHAM HUY T, LE HOANG S. A novel automatic picture fuzzy clustering method based on particle swarm optimization and picture composite cardinality[J]. Knowledge-based systems, 2016, 106:48-60.
[19] 吴成茂, 上官若愚. 嵌入隐马尔科夫随机场的中智模糊聚类算法[J]. 西安电子科技大学学报(自然科学版), 2017, 44(6):113-118 WU Chengmao, SHANGGUAN Ruoyu. Neutrosophic fuzzy clustering segmentation algorithm based on HMRF[J]. Journal of Xidian University (natural sciences), 2017, 44(6):113-118

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备注/Memo

备注/Memo:
收稿日期:2018-06-26。
基金项目:国家自然科学基金项目(61671377,51709228);陕西省自然科学基金项目(2017JM6107);西安邮电大学研究生创新基金项目(CXL2016-14).
作者简介:吴其平,女,1992年生,硕士研究生,主要研究方向为图像处理;吴成茂,男,1968年生,高级工程师,主要研究方向为图像处理与信息安全。主持省部级项目3项。发表学术论文100余篇。
通讯作者:吴其平.E-mail:1739565890@qq.com
更新日期/Last Update: 2019-08-25