Two-stage image segmentation based on nonconvex ℓ2−ℓp approximation and thresholding

Tingting Wu*, Jinbo Shao, Xiaoyu Gu, Michael K. Ng, Tieyong Zeng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

26 Citations (Scopus)

Abstract

Image segmentation is of great importance in image processing. In this paper, we propose a two-stage image segmentation strategy based on the nonconvex ℓ2−ℓp approximation of the Mumford–Shah (MS) model, where we use the nonconvex ℓp (0<p<1) regularizer to approximate the Hausdorff measure and to extract more boundary information. In the first stage, we solve the nonconvex variant of the MS model efficiently via the split-Bregman algorithm. Moreover, we use a closed-form p-shrinkage operator to deal with the ℓp quasi-norm subproblem, which is easy to implement. The second stage is segmenting the u obtained in the first stage into different phases with thresholds determined by the K-means clustering method. We compare our method with several state-of-the-art methods both qualitatively and quantitatively to demonstrate the effectiveness and advantages of our strategy.

Original languageEnglish
Article number126168
JournalApplied Mathematics and Computation
Volume403
DOIs
Publication statusPublished - 15 Aug 2021

Scopus Subject Areas

  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Image segmentation
  • Nonconvex approximation
  • Split–Bregman
  • Two-stage strategy

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