Robust fuzzy local information and Lp-norm distance-based image segmentation method

Fang Li, Jing Qin

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

A variant of fuzzy c-means (FCM) clustering algorithm for image segmentation is provided. Unlike the L2-norm distance in FCM, Lp with p ∈ (0,1] norm is used to measure the distance of the pixel intensity to its cluster centre in the energy functional. Moreover, local spatial information and colour information are incorporated into the model to enhance the robustness to noise and outliers. The proposed algorithm is called fuzzy local information Lp (FLILp) clustering. To overcome the difficulty of finding cluster centres, Lp-norm distance is approximated by weighted L2 distance. The advantages of FLILp are: (i) it is strongly robust to noise and outliers, (ii) it is applied to the original image and (iii) it preserves image edges. Numerical examples and comparisons of image segmentation on both synthetic and real images illustrate the outstanding performance and robustness of the proposed method.

Original languageEnglish
Pages (from-to)217-226
Number of pages10
JournalIET Image Processing
Volume11
Issue number4
DOIs
StatePublished - Apr 1 2017

Bibliographical note

Publisher Copyright:
© The Institution of Engineering and Technology.

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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