An Automatic Image Segmentation Algorithm Based on Weighting Fuzzy C-Means Clustering

  • Yujie Li
  • Huimin Lu
  • Lifeng Zhang
  • Junwu Zhu
  • Shiyuan Yang
  • Xuelong Hu
  • Xiaobin Zhang
  • Yun Li
  • Bin Li
  • Seiichi Serikawa
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 158)

Abstract

Image segmentation is an important research topic in the field of computer vision. Now the fuzzy C-Means (FCM) algorithm is one of the most frequently used clustering algorithms. Although a FCM algorithm is a clustering without supervising, the FCM arithmetic should be given the transcendent information of prototype parameter; otherwise the arithmetic will be wrong. This limits its application in image segmentation. In this paper, we develop a new theoretical approach to automatically selecting the weighting exponent in the FCM to segment the image, which is called Automatic Clustering Weighting Fuzzy C-Means Segmentation (ACWFCM). This method can reduce the disturbance of noise; get the segmentation numbers more accurately. The experimental results illustrate the effectiveness of the proposed method.

Keywords

Image segmentation Fuzzy C-Means clustering Weighting Fuzzy C-Means algorithm Clustering analysis Automatic Clustering Weighting Fuzzy C-Means Segmentation 

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Yujie Li
    • 1
    • 2
  • Huimin Lu
    • 1
  • Lifeng Zhang
    • 1
  • Junwu Zhu
    • 2
  • Shiyuan Yang
    • 1
  • Xuelong Hu
    • 2
  • Xiaobin Zhang
    • 2
  • Yun Li
    • 2
  • Bin Li
    • 2
  • Seiichi Serikawa
    • 1
  1. 1.Department of Electrical Engineering and ElectronicsKyushu Institute of TechnologyKitakyushuJapan
  2. 2.College of Information EngineeringYangzhou UniversityYangzhouChina

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