Fuzzy image clustering incorporating local and region-level information with median memberships

Wang, Qingsheng; Wang, Xiaopeng; Chao, Fang Lin; Jiao, Jianjun

doi:10.1016/j.asoc.2021.107245

Cited by 21 publications

(8 citation statements)

References 57 publications

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“…Select several more advanced algorithms and compare them with the algorithm proposed in this article. Select KWFLICM algorithm [38], APFCM algorithm [39], TFLICM algorithm [40], FALRCM algorithm and its fast algorithm [41]. The comparison result is shown in Figure 13.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…Our work fucuses on segment a picture which contains a lot of noises and need to be segmented into several parts. Previous studies have paid a lot of attention on the robustness of FCM when using on image segmentation [13], [14], [16], [17], [26]. The neighborhood information is fused with the original feature in different ways to get a in these studies.…”

Section: A the Proposed Methodsmentioning

confidence: 99%

“…In [15], original picture is fused with morphological closing reconstruction picture. In [17], original picture is fused with the picture which incorporate local information and region-level information of original picture. In [16], original picture is fused with bilateral filtered picture.…”

Section: B Fcm With Neighborhood Informationmentioning

confidence: 99%

“…In LRFCM [15], tight wavelet transform is used to extract features. In FALRCM [17], local and region-level information is collected in advance to overcome noise. An advance step to smooth picture, or suppress noise, or extract feature and so on can not only get a more accurate result, but consume less resource than making a change in the the objective function of FCM to get the same aims.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Superpixel-based Fuzzy C-Means Method for Complex Picture Segmentation Tasks

Chen

Che

Leung

et al. 2022

2022 14th International Conference on Advanced Computational Intelligence (ICACI)

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Fuzzy c-means(FCM) has attracted wide attentions on picture segmentation as its fuzzy attribute matches the histogram distribution of a picture. However, the fuzzy c-means for the segmentation of a picture with massy noises is barely investigated. In this paper, an improved superpixel-based fuzzy c-means is proposed to segment a massy noise corrupted picture into more than two classes. Firstly, bilateral filtering is used to reduce the compact of noises and makes the picture smoother. Secondly an adaptive method is proposed to fuse the features of the original picture with filtered features. Thirdly simple linearly iterative clustering(SLIC) is used to detect the edge of the picture to avoid over-segmentation. Finally, the histogram-based fuzzy cmeans is used to get the segmentation result. In the experiments, the results show the proposed method achieves a 0.004 ∼ 0.014 higher mPA and 0.004 ∼ 0.06 higher mIoU than other seven algorithms. Besides the segmentation results also show that the over-segmentation is reduced.

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“…By comparing the results of the algorithm in this paper with the classic algorithm in the previous sections, it is found that the optimized algorithm proposed in this paper has better segmentation results when segmenting images contaminated by different types of noise. In this section, we will select several superior algorithms recently published in journals with higher impact factors, including KWFLICM algorithm, APFCM algorithm, TFLICM algorithm, FAL-RCM algorithm and its fast algorithm [52][53][54][55], and compare them with the algorithms in this article.…”

Section: Further Comparison With the Current State-of-the-art Algorithmsmentioning

confidence: 99%

A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation

Song

Tian

et al. 2021

Appl Intell

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Fuzzy clustering algorithm (FCM) can be directly used to segment images, it takes no account of the neighborhood information of the current pixel and does not have a robust segmentation noise suppression. Fuzzy Local Information C-means Clustering (FLICM) is a widely used robust segmentation algorithm, which combines spatial information with the membership degree of adjacent pixels. In order to further improve the robustness of FLICM algorithm, non-local information is embedded into FLICM algorithm and a fuzzy C-means clustering algorithm has local and non-local information (FLICMLNLI) is obtained. When calculating distance from pixel to cluster center, FLICMLNLI algorithm considers two distances from current pixel and its neighborhood pixels to cluster center. However, the algorithm gives the same weight to two different distances, which incorrectly magnifies the importance of neighborhood information in calculating the distance, resulting in unsatisfactory image segmentation effects and loss of image details. In order to solve this problem, we raise an improved self-learning weighted fuzzy algorithm, which directly obtains different weights in distance calculation through continuous iterative self-learning, then the distance metric with the weights obtained from self-learning is embedded in the objective function of the fuzzy clustering algorithm in order to improve the segmentation performance and robustness of the algorithm. A large number of experiments on different types of images show that the algorithm can not only suppress the noise but also retain the details in the image, the effect of segmenting complex noise images is better, and it provides better image segmentation results than the existing latest fuzzy clustering algorithms.

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Fuzzy image clustering incorporating local and region-level information with median memberships

Cited by 21 publications

References 57 publications

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

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

An Improved Superpixel-based Fuzzy C-Means Method for Complex Picture Segmentation Tasks

A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation

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