基于模糊聚类分析的图像分割算法研究 (1).doc

基于模糊聚类分析的图像分割算法研究
摘要
模糊聚类分析具备将不确定性样本进行分类的功能,近年来被广泛地应用于图像分
割算法研究中。通过对模糊 c 均值聚类算法(FCM)这一现今最经典的模糊聚类算法进
行改进,验证了本文算法的有效性。
首先,标准 FCM 算法执行的前提是聚类数的确定。不同的图像聚类数也不同,聚类
数的选取对分割至关重要。研究人员通过图像的直方图波峰的个数来对应图像的聚类数,
但受噪声点影响,使得像素属于某一灰度值是不清晰的,容易导致误差的产生。考虑到
模糊集理论能够体现像素属于某一灰度值的程度大小,用高斯函数替代脉冲函数,分别
构造了模糊直方图和模糊 Histon 直方图,充分利用了像素的灰度信息和空间信息。
其次,利用像素的灰度信息和局部空间信息可以在一定程度上抑制噪声的影响,但
当图像被大量噪声严重侵扰时,像素提供的局部空间信息不能体现明显优势。针对这个
问题,提出了结合模糊直方图的非局部空间自适应 FCM 算法,合理利用像素提供的非局
部空间信息,考虑到提出算法的目标函数中,对隶属度矩阵及聚类中心迭代公式进行改
进,提出的算法在分割质量和效果上更好。
最后,在 Lab 彩色空间中通过模糊 Histon 阈值技术获取整幅图像中所有可能的由聚
类中心标注的均匀区域,提出了自适应 FCM 聚类算法用来改善均匀区域之间的紧密度,
完成色彩分割,已成功应用到伯克利图像库,取得了更好的划分。考虑到颜色相近而语
义无关的影响,在像素特征中加入纹理特征,提出了结合亮度、颜色、纹理特征的 FCM
色彩分割算法,相比标准 FCM 算法实现了彩***像的较优分割,具有一定抗噪性。
关键词:FCM 聚类算法;非局部空间信息;模糊 Histon 直方图;纹理特征
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万方数据
基于模糊聚类分析的图像分割算法研究
Abstract
Fuzzy clustering analysis has the ability of classifying samples with uncertainty and has
been widely applied in image segmentation in recent years. An improved fuzzy clustering
algorithm was proposed in this paper based on classical fuzzy c-means (FCM) algorithm and
the validity of the new algorithm was verified.
Firstly, an open problem of standard FCM is to determine the number of clusters in
advance. Different images have different amounts of cluster, thus selecting appropriate number
of clusters is crucial to the results of image segmentation. Usually, researchers determine the
number of clusters by the number of histogram peaks of an image. However, influenced by
noise, this method easily yields error because the concept that a pixel belongs to a gray level is
not clear. Considering that fuzzy set theory can reflect the degree of a pixel belonging to a gray
level, fuzzy histogram and fuzzy histon histogram are constructed respectively in the gray
images and color images by replacing impulse function to Gaussian function and making full
use of gray information and spatial information of pixels.
Secondly, although gray information and local spatial information of e