Adaptive entropy weighted picture fuzzy clustering algorithm with spatial information for image segmentation

Wu, Chengmao; Chen, Yan

doi:10.1016/j.asoc.2019.105888

Cited by 33 publications

(9 citation statements)

References 22 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%

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Kernel-based fuzzy local information clustering algorithm self-integrating non-local information

Song

Tian

et al. 2022

Digital Signal Processing

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Section: Resultsmentioning

confidence: 99%

“…The greater the accuracy, sensitivity, specificity, and precision, the better the segmentation effect. • Misclassification error (ME) [35]as a quantitative evaluation index can objectively and quantitatively describe the effectiveness and robustness of the algorithm.…”

Section: Evaluation Indexmentioning

confidence: 99%

Kernel-based fuzzy local information clustering algorithm self-integrating non-local information

Song

Tian

et al. 2022

Digital Signal Processing

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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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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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“…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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Adaptive entropy weighted picture fuzzy clustering algorithm with spatial information for image segmentation

Cited by 33 publications

References 22 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

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

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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