Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation

Fasahat Ullah Siddiqui, Nor Ashidi Mat Isa, Abid Yahya

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper presents a fuzzy clustering-based technique for image segmentation. Many attempts have been put into practice to increase the conventional fuzzy c-means (FCM) performance. In this paper, the sensitivity of the soft membership function of the FCM algorithm to the outlier is considered and the new exponent operator on the Euclidean distance is implemented in the membership function to improve the outlier rejection characteristics of the FCM. The comparative quantitative and qualitative studies are performed among the conventional k-means (KM), moving KM, and FCM algorithms; the latest state-of-the-art clustering algorithms, namely the adaptive fuzzy moving KM , adaptive fuzzy KM, and new weighted FCM algorithms; and the proposed outlier rejection FCM (ORFCM) algorithm. It is revealed from the experimental results that the ORFCM algorithm outperforms the other clustering algorithms in various evaluation functions.

Original languageEnglish
Pages (from-to)1801-1819
Number of pages19
JournalTurkish Journal of Electrical Engineering and Computer Sciences
Volume21
Issue number6
DOIs
Publication statusPublished - Oct 21 2013

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Image segmentation
Membership functions
Clustering algorithms
Function evaluation
Fuzzy clustering

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

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Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation. / Siddiqui, Fasahat Ullah; Isa, Nor Ashidi Mat; Yahya, Abid.

In: Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 21, No. 6, 21.10.2013, p. 1801-1819.

Research output: Contribution to journalArticle

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