Two-Stage Image Segmentation Scheme Based on Inexact Alternating Direction Method

Zhi, Zhanjiang; Sun, Yi; Pang, Zhi-Feng

doi:10.4208/nmtma.2016.m1509

Cited by 5 publications

(6 citation statements)

References 33 publications

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“…Since the SaT strategy is a flexible methodology for image segmentation, it has been adopted and followed by many papers [17][18][19][24][25][26][27][28][29]. And the strategy was extended to images FIGURE 1 Segmentation results of Flowers with Gaussian noise (mean μ = 0 and variance 𝜎 2 = 0.1).…”

Section: Introductionmentioning

confidence: 99%

Colour image segmentation based on a convex K‐means approach

Shao

et al. 2021

IET Image Processing

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Image segmentation is a fundamental and challenging task in image processing and computer vision. The colour image segmentation is attracting more attention as the colour image provides more information than the grey image. A variational model based on a convex K-means approach to segment colour images is proposed. The proposed variational method uses a combination of l 1 and l 2 regularizers to maintain edge information of objects in images while overcoming the staircase effect. Meanwhile, our one-stage strategy is an improved version based on the smoothing and thresholding strategy, which contributes to improving the accuracy of segmentation. The proposed method performs the following steps. First, the colour set which can be determined by human or the K-means method is specified. Second, a variational model to obtain the most appropriate colour for each pixel from the colour set via convex relaxation and lifting is used. The Chambolle-Pock algorithm and simplex projection are applied to solve the variational model effectively. Experimental results and comparison analysis demonstrate the effectiveness and robustness of the method. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Colour image segmentation based on a convex K‐means approach

Shao

et al. 2021

IET Image Processing

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“…Since the SaT strategy is a flexible methodology for image segmentation, it has been adopted and followed by many papers [26], [19], [20], [27], [28], [29], [30], [31], [21]. And the strategy was extended to images corrupted by Poisson or Gamma noises [32], color images [1] and classification for high-dimensional data and point clouds.…”

Section: Introductionmentioning

confidence: 99%

Color image segmentation based on a convex K-means approach

Wu¹,

Gu²,

Shao³

et al. 2021

Preprint

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Image segmentation is a fundamental and challenging task in image processing and computer vision. The color image segmentation is attracting more attention due to the color image provides more information than the gray image. In this paper, we propose a variational model based on a convex K-means approach to segment color images. The proposed variational method uses a combination of l1 and l2 regularizers to maintain edge information of objects in images while overcoming the staircase effect. Meanwhile, our onestage strategy is an improved version based on the smoothing and thresholding strategy, which contributes to improving the accuracy of segmentation. The proposed method performs the following steps. First, we specify the color set which can be determined by human or the K-means method. Second, we use a variational model to obtain the most appropriate color for each pixel from the color set via convex relaxation and lifting. The Chambolle-Pock algorithm and simplex projection are applied to solve the variational model effectively. Experimental results and comparison analysis demonstrate the effectiveness and robustness of our method.

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“…On the other hand, since +1 is an optimum of the convex function , we can know from (21) and Taylor series expansion that…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Recently, the alternating direction method [26] is used to solve the 2 -TV and 1 -TV image restoration models, and better performance has been obtained [18][19][20][21][22][23]27]. It divides original problem into some simple subproblems and solves them alternatively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Total Variation Image Restoration Method Based on Subspace Optimization

Liu

Gao

2018

Mathematical Problems in Engineering

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The alternating direction method is widely applied in total variation image restoration. However, the search directions of the method are not accurate enough. In this paper, one method based on the subspace optimization is proposed to improve its optimization performance. This method corrects the search directions of primal alternating direction method by using the energy function and a linear combination of the previous search directions. In addition, the convergence of the primal alternating direction method is proven under some weaker conditions. Thus the convergence of the corrected method could be easily obtained since it has same convergence with the primal alternating direction method. Numerical examples are given to show the performance of proposed method finally.

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Two-Stage Image Segmentation Scheme Based on Inexact Alternating Direction Method

Cited by 5 publications

References 33 publications

Colour image segmentation based on a convex K‐means approach

Colour image segmentation based on a convex K‐means approach

Color image segmentation based on a convex K-means approach

Total Variation Image Restoration Method Based on Subspace Optimization

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