基于核与局部信息的多维度模糊聚类图像分割算法

doi:10.11772/j.issn.1001-9081.2015.11.3227

计算机应用 ›› 2015, Vol. 35 ›› Issue (11): 3227-3231.DOI: 10.11772/j.issn.1001-9081.2015.11.3227

• 第十五届中国Rough集与软计算学术会议(CRSSC 2015)论文 • 上一篇下一篇

基于核与局部信息的多维度模糊聚类图像分割算法

王少华¹, 狄岚¹, 梁久祯²

1. 江南大学数字媒体学院, 江苏无锡 214122;
2. 江南大学物联网工程学院, 江苏无锡 214122

收稿日期:2015-06-09 修回日期:2015-06-26 发布日期:2015-11-13
通讯作者: 王少华(1991-),男,江西九江人,硕士研究生,主要研究方向:数字图像处理、数据挖掘.
作者简介:狄岚(1965-),女,江苏南京人,副教授,硕士,CCF会员,主要研究方向:模式识别、数字图像处理; 梁久祯(1968-),男,山东泰安人,教授,博士, CCF会员,主要研究方向:计算机视觉、模式识别.
基金资助:
江苏省六大人才高峰项目(DZXX-028),江苏省产学研项目(BY2014023-33).

Multi-dimensional fuzzy clustering image segmentation algorithm based on kernel metric and local information

WANG Shaohua¹, DI Lan¹, LIANG Jiuzhen²

1. College of Digital Media, Jiangnan University, Wuxi Jiangsu 214122, China;
2. College of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China

Received:2015-06-09 Revised:2015-06-26 Published:2015-11-13

摘要/Abstract

摘要： 在以聚类分析为背景的图像分割算法中,引入局部信息是为了在保留图像细节的同时尽可能地减少噪声.在模糊C均值算法基础上,提出了一种基于核与局部信息的多维度模糊聚类分析方法来权衡图像中的噪声和细节.该算法引入2个基于局部信息的图像变体,即平滑和锐化处理后的图像,使之与原始图像一起构成多维度的灰度值向量来替换原始单维的灰度值; 再利用核方法提高其鲁棒性; 最后添加一个邻域隶属度差异惩罚项很好地修正和增强了最终的分割效果.在人工合成图片的去噪实验中,所提方法取得了近99%的分割正确率,优于Nystrom归一化分割(NNcut)和基于模糊局部信息C均值(FLICM)算法;同时在自然图片和医学图片的对比实验以及参数调控实验中,展现出了其在处理图像噪声和细节时灵活、稳定、健壮且易于调控的特点.

关键词: 聚类分析, 图像分割, 模糊C均值, 多维度, 核方法, 局部信息

Abstract: In image segmentation based on clustering analysis, spatial constraints were imposed so as to reduce noise but preserve details. Based on Fuzzy C-Means (FCM) method, a multi-dimensional fuzzy clustering image segmentation algorithm based on kernel metric and local information was proposed to compromise noise and details in the image. In the algorithm, two extra images based on local information derived from the original one through a smoothing and a sharpening filter respectively were introduced to construct a multi-dimensional gray level vector to replace the original one-dimensional gray level. And then kernel method was employed to strengthen its robustness. In addition, a penalty term, which represents the diversity between local pixel and its neighbors, was used to modify the objective function so as to improve its anti-noise ability further. Compared with NNcut (Nystrom Normalized cut) and FLICM (Fuzzy Local Information C-Means), its segmentation accuracy achieved almost 99%. The experimental results on natural and medical images and parameter adjusting demonstrate its favorable advantages of flexibility and robustness when dealing with noise and details.

Key words: clustering analysis, image segmentation, Fuzzy C-Means (FCM) method, multi-dimension, kernel metric, local information

中图分类号:

TP391.4

王少华, 狄岚, 梁久祯. 基于核与局部信息的多维度模糊聚类图像分割算法[J]. 计算机应用, 2015, 35(11): 3227-3231.

WANG Shaohua, DI Lan, LIANG Jiuzhen. Multi-dimensional fuzzy clustering image segmentation algorithm based on kernel metric and local information[J]. Journal of Computer Applications, 2015, 35(11): 3227-3231.

参考文献

[1] FU K S, MUI J K. A survey on image segmentation[J]. Pattern Recognition, 1981, 13(1):3-16.
[2] HARALICK R M, SHAPIRO L G. Image segmentation techniques[J]. Computer Vision Graphics and Image Processing, 1985, 29(85):100-132.
[3] PAL N R, PAL S K. A review on image segmentation techniques[J]. Pattern Recognition, 1993, 26(93):1277-1294.
[4] ZHANG Y J. A survey on evaluation methods for image segmentation[J]. Pattern Recognition, 1996, 29(95):1335-1346.
[5] CHENG H, JIANG X, SUN Y, et al. Color image segmentation: advances and prospects[J]. Pattern Recognition, 2001, 34(1):2259-2281.
[6] RUSPINI E H. A new approach to clustering[J]. Information and Control, 1969, 15(69):22-32.
[7] DUNN J C. A graph theoretic analysis of pattern classification via Tamura's fuzzy relation[J]. IEEE Transactions on Systems Man and Cybernetics, 1974, 4(3):310-313.
[8] BACKER E, JAIN A K. A clustering performance measure based on fuzzy set decomposition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1981, 3(1):66-75.
[9] WU Z, LEAHY R. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(11):1101-1113.
[10] LE K. Fuzzy relation compositions and pattern recognition[J]. Information Sciences, 1996, 89(24): 107-130.
[11] DUNN J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of Cybernetics, 1974, 3(3):32-57.
[12] BEZDEK J C. Pattern recognition with fuzzy objective function algorithms[M]. New York: Plenum Press, 1981:15-45.
[13] TOLIAS Y A, PANAS S M. On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system[J]. IEEE Signal Processing Letters, 1998, 5(10):245-247.
[14] TOLIAS Y A, PANAS S M. Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions[J]. IEEE Transactions on Systems Man and Cybernetics Part A: Systems and Humans, 1998, 28(3):359-369.
[15] AHMED M N, YAMANY S M, MOHAMED N, et al. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging, 2002, 21(3):193-199.
[16] CHEN S, ZHANG D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Transactions on Systems Man and Cybernetics Part B, 2004, 34(4):1907-1916.
[17] CAI W, CHEN S, ZHANG D. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J]. Pattern Recognition, 2007, 40(3):825-838.
[18] LUO Z, FENG G, CHENG Q. Application of clustering and improved C-V evolution equation in medical image segmentation[J]. Journal of Computer Applications, 2007, 28(9): 2288-2291.(罗志宏, 冯国灿, 成秋生. 结合聚类和改进的C-V演化方程在医学图像分割中的应用[J]. 计算机应用, 2008, 28(9):2288-2291.)
[19] KRINIDIS S, CHATZIS V. A robust fuzzy local information c-means clustering algorithm[J]. IEEE Transactions on Image Processing, 2010, 19(5):1328-1337.
[20] HAVENS T C, CHITTA R, JAIN A K, et al. Speedup of fuzzy and possibilistic kernel c-means for large-scale clustering[C]// Proceedings of the 2011 IEEE International Conference on Fuzzy Systems. Piscataway: IEEE, 2011:463-470.
[21] CHEN L, CHEN C L P, LU M. A multiple-kernel fuzzy c-means algorithm for image segmentation[J]. IEEE Transactions on Systems Man and Cybernetics Part B, 2011, 41(5):1263-1274.
[22] GONG M, ZHOU Z, MA J. Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering[J]. IEEE Transactions on Image Processing, 2012, 21(4):2141-2151.
[23] TSAI D, LIN C. Fuzzy c-means based clustering for linearly and nonlinearly separable data[J]. Pattern Recognition, 2011, 44(8):1750-1760.
[24] GONG M, LIANG Y, SHI J, et al. Fuzzy c-means clustering with local information and kernel metric for image segmentation[J]. IEEE Transactions on Image Processing, 2013, 22(2):573-584.
[25] WANG H, LIU M. Generalized fuzzy c-means algorithm with improved fuzzy partitions based on local membership and neighbor information[J]. Journal of Computer Applications, 2013, 33(8): 2355-2358. (王海军, 柳明. 基于局部隶属度和邻域信息的GIFP-FCM图像分割算法[J]. 计算机应用, 2013, 33(8):2355-2358.
[26] ZHANG D Q, CHEN S C. Kernel-based fuzzy and possibilistic c-means clustering[C]// Proceedings of the International Conference Artificial Neural Network. Montréal: ICAN, 2003: 122-125.
[27] YANG Y, ZHENG C, LIN P. Fuzzy c-means clustering algorithm with a novel penalty term for image segmentation[J]. Optoelectronics Review, 2005, 13(4):309-315.
[28] FOWLKES C, BELONGIE S, CHUNG F, et al. Spectral grouping using the Nystrom method[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(2):214-225.

基于核与局部信息的多维度模糊聚类图像分割算法

Multi-dimensional fuzzy clustering image segmentation algorithm based on kernel metric and local information

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