Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (11): 3227-3231.DOI: 10.11772/j.issn.1001-9081.2015.11.3227

• CRSSC 2015 Paper • Previous Articles     Next Articles

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

WANG Shaohua1, DI Lan1, LIANG Jiuzhen2   

  1. 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

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. 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
  • 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).

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

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

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