A Two-Stage Image Segmentation Model for Multi-Channel Images

Li, Zhi; Zeng, Tieyong

doi:10.4208/cicp.260115.200715a

Cited by 9 publications

(18 citation statements)

References 44 publications

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“…Traditional hill-climbing segmentation [41,42] is a simple, fast algorithm that clusters colours of an image without any hand tuning of parameters. Li et al made a few modifications to get a more stable hill-climbing procedure [18]. Moreover, the most significant advantage of hill climbing is that it does not need prior knowledge of the number of clusters or the content of the given image and can detect the number of segments.…”

Section: Hill-climbing Methodsmentioning

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: Hill-climbing Methodsmentioning

confidence: 99%

Colour image segmentation based on a convex K‐means approach

Shao

et al. 2021

IET Image Processing

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“…Traditional hill-climbing segmentation [41], [42] is a simple, fast algorithm that clusters colors of an image without any hand-tuning of parameters. Li et al made a few modifications to get a more stable hill-climbing procedure [20]. Moreover, the most significant advantage of hill-climbing is that it does not need prior knowledge of the number of clusters or the content of the given image and can detect the number of segments.…”

Section: B Hill-climbing Methodsmentioning

confidence: 99%

“…Image segmentation aims at dividing an image of N pixels into K regions with similar characteristics together (edges, intensities, colors or textures). Various models and algorithms have been extensively applied for image segmentation, including level-set methods [7], [8], [9], [10], [11], [12], [13], active contours [14], [15], [16], [17], variational models [18], [19], [20], [21] and clustering methods [22], [23], [24], etc. Particularly, many successful methods for image segmentation are based on variational models and clustering methods.…”

Section: Introductionmentioning

confidence: 99%

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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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“…However, it is very difficult to control the trade-off between smoothing and clustering. Thus, many other approaches consider to incorporate local spatial information into FCM to enhance the segmentation accuracy (SA) [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. In this paper, we focus on the FCM variants which have the simple solution framework as FCM.…”

Section: Introductionmentioning

confidence: 99%

Robust fuzzy local information and ‐norm distance‐based image segmentation method

Qin

2017

IET Image Processing

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A Two-Stage Image Segmentation Model for Multi-Channel Images

Cited by 9 publications

References 44 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

Robust fuzzy local information and ‐norm distance‐based image segmentation method

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