Segmentation method for affinity propagation clustering images based on fuzzy connectedness

doi:10.11772/j.issn.1001-9081.2014.11.3309

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (11): 3309-3313.DOI: 10.11772/j.issn.1001-9081.2014.11.3309

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Segmentation method for affinity propagation clustering images based on fuzzy connectedness

DU Yanxin,GE Hongwei,XIAO Zhiyong

Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education (Jiangnan University), Wuxi Jiangsu 214122, China;

Received:2014-05-23 Revised:2014-06-28 Online:2014-12-01 Published:2014-11-01
Contact: DU Yanxin

基于模糊连接度的近邻传播聚类图像分割方法

杜艳新,葛洪伟,肖志勇

轻工过程先进控制教育部重点实验室(江南大学),江苏无锡 214122

通讯作者: 杜艳新
作者简介:
杜艳新(1990-),男,河北承德人,硕士研究生,主要研究方向:人工智能、模式识别、图像处理;葛洪伟(1967-),男,江苏无锡人,教授,博士生导师,主要研究方向:人工智能、模式识别、图像处理、信息管理、数据挖掘;肖志勇(1986-),男,河南汤阴人,副教授,主要研究方向:医学图像处理、模式识别、机器学习。
基金资助:
江苏高校优势学科建设工程项目;江苏高校优势学科建设工程项目

Abstract

Abstract:

Considering the low accuracy of the existing image segmentation method based on affinity propagation clustering, a FCAP algorithm which combined fuzzy connectedness and affinity propagation clustering was proposed. A Whole Fuzzy Connectedness (WFC) algorithm was also proposed with concerning the shortcoming of traditional fuzzy connectedness algorithms that can not get fuzzy connectedness of every pair of pixels. In FCAP, the image was segmented by using super pixel technique. These super pixels could be considered as data points and their fuzzy connectedness could be computed by WFC. Affinities between super pixels could be calculated based on their fuzzy connectedness and spatial distances. Finally, affinity propagation clustering algorithm was used to complete the segmentation. The experimental results show that FCAP is much better than the methods which use affinity propagation clustering directly after getting super pixels, and can achieve competitive performance when comparing with other unsupervised segmentation methods.

摘要：

针对现有近邻传播聚类图像分割方法分割精度低的问题,提出一种基于模糊连接度的邻近传播聚类(FCAP)图像分割算法。针对传统模糊连接度算法不能得出任意点对间模糊连接度的不足,结合最大生成树提出了全模糊连接度算法。FCAP算法先使用Normalized Cut超像素技术进行超像素分割，这些超像素可以看作数据点以及它们之间的模糊连接度;然后使用所提出的全模糊连接度算法计算超像素间的模糊连接度,根据模糊连接度和空间信息计算超像素的相似度;最后使用近邻传播(AP)聚类算法完成分割。实验结果表明,FCAP算法明显优于超像素处理后直接使用AP聚类算法进行分割的方法,并且优于无监督图像分割方法。

CLC Number:

TP391.41

DU Yanxin GE Hongwei XIAO Zhiyong. Segmentation method for affinity propagation clustering images based on fuzzy connectedness[J]. Journal of Computer Applications, 2014, 34(11): 3309-3313.

杜艳新葛洪伟肖志勇. 基于模糊连接度的近邻传播聚类图像分割方法[J]. 计算机应用, 2014, 34(11): 3309-3313.

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Segmentation method for affinity propagation clustering images based on fuzzy connectedness

基于模糊连接度的近邻传播聚类图像分割方法

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