王昭, 娄昊, 范九伦. 一种基于模C均值的图像超像素分割方法[J]. 微电子学与计算机, 2014, 31(10): 18-21,27.
引用本文: 王昭, 娄昊, 范九伦. 一种基于模C均值的图像超像素分割方法[J]. 微电子学与计算机, 2014, 31(10): 18-21,27.
WANG Zhao, LOU Hao, FAN Jiu-lun. Fuzzy C-Means Clustering Algorithm with Superpivel for Image Segmentation[J]. Microelectronics & Computer, 2014, 31(10): 18-21,27.
Citation: WANG Zhao, LOU Hao, FAN Jiu-lun. Fuzzy C-Means Clustering Algorithm with Superpivel for Image Segmentation[J]. Microelectronics & Computer, 2014, 31(10): 18-21,27.

一种基于模C均值的图像超像素分割方法

Fuzzy C-Means Clustering Algorithm with Superpivel for Image Segmentation

  • 摘要: 在分析现有图像分割算法基础上,提出一种基于超像素的模糊C均值分割算法.首先利用像素间灰度和距离定义像素间相似度,从而循环迭代出图像的超像素;然后进一步提取每个超像素的小波能量特征并利用模糊C均值算法对该特征进行聚类.大量实验表明,提出的图像分割算法对噪声有一定稳健性,分割准确率高,并能有效抑制孤立点的影响.

     

    Abstract: Based on the analysis of common segmentation methods,a fuzzy C-means (FCM) clustering algorithm with superpixel is proposed in this paper.Firstly,a kind of pixel similarity considering intensity and distance is defined and the image superpixels are generated.After that,the wavelet energy features are extracted from the superpixels,which are clustered by FCM.The experimental results demonstrate that the proposed method is robust to noise and isolated pixels,which can improve the segmentation accuracy.

     

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