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 language | English |
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Article number | 126168 |
Journal | Applied Mathematics and Computation |
Volume | 403 |
DOIs | |
Publication status | Published - 15 Aug 2021 |
Scopus Subject Areas
- Computational Mathematics
- Applied Mathematics
User-Defined Keywords
- Image segmentation
- Nonconvex approximation
- Split–Bregman
- Two-stage strategy