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Kernel Fuzzy Similarity Measure-Based Spectral Clustering for Image Segmentation

  • Yifang Yang
  • Yuping Wang
  • Yiu-ming Cheung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8008)

Abstract

Spectral clustering has been successfully used in the field of pattern recognition and image processing. The efficiency of spectral clustering, however, depends heavily on the similarity measure adopted. A widely used similarity measure is the Gaussian kernel function where Euclidean distance is used. Unfortunately, the Gaussian kernel function is parameter sensitive and the Euclidean distance is usually not suitable to the complex distribution data. In this paper, a novel similarity measure called kernel fuzzy similarity measure is proposed first, Then this novel measure is integrated into spectral clustering to get a new clustering method: kernel fuzzy similarity based spectral clustering (KFSC). To alleviate the computational complexity of KFSC on image segmentation, Nystr\(\ddot{o}\)m method is used in KFSC. At last, the experiments on three synthetic texture images are made, and the results demonstrate the effectiveness of the proposed algorithm.

Keywords

spectral clustering kernel fuzzy-clustering image segmentation Nyström method 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yifang Yang
    • 1
  • Yuping Wang
    • 1
  • Yiu-ming Cheung
    • 2
  1. 1.School of Computer Science and TechnologyXidian UniversityXi’anChina
  2. 2.Department of Computer ScienceHong Kong Baptist UniversityHong Kong

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