Robust Self-Sparse Fuzzy Clustering for Image Segmentation

Jia, Xiaohong; Lei, Tao; Du, Xiaogang; Liu, Shigang; Meng, Hongying; Nandi, Asoke K.

doi:10.1109/access.2020.3015270

Cited by 59 publications

(44 citation statements)

References 51 publications

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“…To quantitative comparison of the segmentation performance, we use one objective indicator, that is, the SA [33]. The SA is computed as follows:…”

Section: Methodsmentioning

confidence: 99%

“…The RSSFC presented a method of robust self‐sparse fuzzy clustering for image segmentation. For all methods in experiments, the scope of the main parameter is provided as follows: (a)FRC [49]: the regularization parameter

λ \in

[0.0001, 1]; (b)DPP [50]: the regularization parameter

λ \in

[0.1, 5]; (c)SLaT [31]: the regularization parameter

λ \in

[1, 10]; (d)LC [36]: the regularization parameter

λ \in

[0.001, 10]; (e)CKC [37]: the regularization parameter

λ \in

[1, 500]; (f)RSSFC [33]: the regularization parameter

λ \in

[0.01, 1]; (g)Proposed: the regularization parameter

λ \in

[0.0001, 1]. …”

Section: Methodsmentioning

confidence: 99%

“…Jia et al . [33] presented a robust self‐sparse fuzzy clustering algorithm for solving over‐segmentation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…To quantitative comparison of the segmentation performance, we use one objective indicator, that is, the SA [33]. The SA is computed as follows:…”

Section: Methodsmentioning

confidence: 99%

λ \in

[0.0001, 1]; (b)DPP [50]: the regularization parameter

λ \in

[0.1, 5]; (c)SLaT [31]: the regularization parameter

λ \in

[1, 10]; (d)LC [36]: the regularization parameter

λ \in

[0.001, 10]; (e)CKC [37]: the regularization parameter

λ \in

[1, 500]; (f)RSSFC [33]: the regularization parameter

λ \in

[0.01, 1]; (g)Proposed: the regularization parameter

λ \in

[0.0001, 1]. …”

Section: Methodsmentioning

confidence: 99%

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Colour image segmentation based on a convex K‐means approach

Shao

et al. 2021

IET Image Processing

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show abstract

“…Currently, clustering has been widely used in different fields such as image processing [1], pattern classification [2,3], deep learning [4], etc. Among different clustering methods, fuzzy c-means (FCM) is one of the most popular methods due to its simplicity and efficiency [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy STUDENT’S T-Distribution Model Based on Richer Spatial Combination

Lei

Jia

Xue

et al. 2022

IEEE Trans. Fuzzy Syst.

Self Cite

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Fuzzy c-means (FCM) algorithms with spatial information have been widely applied in the field of image segmentation. However, most of them suffer from two challenges. One is that introduction of fixed or adaptive single neighboring information with narrow receptive field limits contextual constraints leading to clutter segmentations. The other is that incorporation of superpixels with wide receptive field enlarges spatial coherency leading to block effects. To address these challenges, we propose fuzzy Student's t-distribution model based on richer spatial combination (FRSC) for image segmentation. In this Paper, we make two significant contributions. The first is that both narrow and wide receptive fields are integrated into the objective function of FRSC, which is convenient to mine image features and distinguish local difference. The second is that the rich spatial combination under Student's t-distribution ensures that spatial information is introduced into the updated parameters of FRSC, which is helpful in finding a balance between the noise-immunity and detail-preservation. Experimental results on synthetic and publicly available images, further demonstrate that the proposed FRSC addresses successfully the limitations of FCM algorithms with spatial information and provides better segmentation results than state-of-the-art clustering algorithms.

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“…To solve these problems, scholars have proposed many improved fuzzy clustering algorithms. There are two main strategies used, one is to use the regularization method to realize the self-optimization of FCM, and the other is to include local spatial information into the object function of FCM to improve image segmentation [14].Yang and Tsai et al [15]extended FCM to the kernel space, and obtained a kernel-based fuzzy C-means clustering algorithm (KFCM). However, FCM and KFCM did not consider the spatial neighborhood information of the current pixel, resulting in poor clustering results of noise images.…”

Section: Introductionmentioning

confidence: 99%

Kernel-Based Fuzzy Local Information Clustering Algorithm Self-integrating Non-Local Information

Song¹,

Wu²,

Tian³

et al. 2021

Preprint

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The application of fuzzy clustering algorithms in image segmentation is a hot research topic nowadays. Existing fuzzy clustering algorithms have the following three problems: (1)The parameters of spatial information constraints can$'$t be selected adaptively; (2)The image corrupted by high noise can$'$t be segmented effectively; (3)It is difficult to achieve a balance between noise removal and detail preservation. In the fuzzy clustering based on the optimization model, the choice of distance metric is very important. Since the use of Euclidean distance will lead to sensitivity to outliers and noise, it is difficult to obtain satisfactory segmentation results, which will affect the clustering performance. This paper proposes an optimization algorithm based on the kernel-based fuzzy local information clustering integrating non-local information (KFLNLI). The algorithm adopts a self-integration method to introduce local and non-local information of images, which solves the common problems of current clustering algorithm. Firstly, the self-integration method solves the problem of selecting spatial constraint parameters. The algorithm uses continuous self-learning iteration to calculate the weight coefficients; Secondly, the distance metric uses Gaussian kernel function to induce the distance to further enhance the robustness against noise and the adaptivity of processing different images; Finally, both local and non-local information are introduced to achieve a segmentation effect that can eliminate most of the noise and retain the original details of the image. Experimental results show that the algorithm is superior to existing state-of-the-art fuzzy clustering-related algorithm in the presence of high noise.

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Robust Self-Sparse Fuzzy Clustering for Image Segmentation

Cited by 59 publications

References 51 publications

Colour image segmentation based on a convex K‐means approach

Colour image segmentation based on a convex K‐means approach

Fuzzy STUDENT’S T-Distribution Model Based on Richer Spatial Combination

Kernel-Based Fuzzy Local Information Clustering Algorithm Self-integrating Non-Local Information

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