Image segmentation using fuzzy region competition and spatial/frequency information

S. K. Choy, Man Lai TANG, Chong Sze TONG

Research output: Contribution to journalJournal articlepeer-review

29 Citations (Scopus)


This paper presents a multiphase fuzzy region competition model that takes into account spatial and frequency information for image segmentation. In the proposed energy functional, each region is represented by a fuzzy membership function and a data fidelity term that measures the conformity of spatial and frequency data within each region to (generalized) Gaussian densities whose parameters are determined jointly with the segmentation process. Compared with the classical region competition model, our approach gives soft segmentation results via the fuzzy membership functions, and moreover, the use of frequency data provides additional region information that can improve the overall segmentation result. To efficiently solve the minimization of the energy functional, we adopt an alternate minimization procedure and make use of Chambolle's fast duality projection algorithm. We apply the proposed method to synthetic and natural textures as well as real-world natural images. Experimental results show that our proposed method has very promising segmentation performance compared with the current state-of-the-art approaches.

Original languageEnglish
Article number5643926
Pages (from-to)1473-1484
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number6
Publication statusPublished - Jun 2011

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Generalized Gaussian density
  • region competition
  • segmentation


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