Block-Matching Fuzzy C-Means clustering algorithm for segmentation of color images degraded with Gaussian noise

Gamino-Sánchez, Fernando; Hernández-Gutiérrez, Isabel V.; Rosales, Alberto; Gallegos-Funes, Francisco J.; Mújica-Vargas, Dante; Ramos-Diaz, Eduardo; Carvajal-Gámez, Blanca E.; Kinani, Jean Marie Vianney

doi:10.1016/j.engappai.2018.04.026

Cited by 25 publications

(6 citation statements)

References 37 publications

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“…The introduced proposal is compared with iterative methods based on clustering processes. Specifically, pareto‐based interval Type‐2 fuzzy C‐means with multi‐scale just noticeable difference colour histogram (PIT2FC‐MJND) [7], fuzzy C‐means with extracting chromaticity features of colours (FCMECFC) [8], improved FCM algorithm based on the morphological reconstruction and membership filtering (FRFCM) [9], image segmentation a method based on fast density clustering algorithm (IS‐FDC) [10] and block‐matching fuzzy C‐means (BMFCM) [11]. The considered metrics were misclassification ratio (MCR), dice similarity coefficient for image segmentation, intersection‐over‐union (IOU), the runtime is not considered in this document.…”

Section: Resultsmentioning

confidence: 99%

Robust Gaussian‐base radial kernel fuzzy clustering algorithm for image segmentation

et al. 2019

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To perform the image segmentation task, in this Letter, a kernel fuzzy C-means algorithm is introduced, strengthened by a robust Gaussian radial basis function kernel based on M-estimators. It is well-known that these kernels consider the squared difference as a similarity measure, which is not robust to atypical data. In this regard, the main motivation of this contribution is to improve the atypical information tolerance of these kernels, in order to make a better clustering of pixels. Experimental tests were developed considering colour images. The robustness and effectiveness of this proposal are verified by quantitative and qualitative results.

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Section: Resultsmentioning

confidence: 99%

Robust Gaussian‐base radial kernel fuzzy clustering algorithm for image segmentation

et al. 2019

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“…To assess the performance of the proposed method, we further tested it on natural images from the BSDS and MSRC datasets (see Table 2 ). The both mentioned datasets are the most popular benchmarks and they are widely used by researchers for color image segmentation [ 27 , 39 , 40 ]. The results reported are averaged after 10 experiments and illustrated by Figure 20 .…”

Section: Application On Fire Forest Imagesmentioning

confidence: 99%

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Tlig¹,

Bouchouicha

Tlig

et al. 2020

Sensors

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Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

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“…In the calculation process of the whole fuzzy connection theory framework, the preprocessing method, the selection method of seed points, the definition method of fuzzy affinity, and the method of threshold segmentation are the key points, except for the definition of fuzzy affinity. Three points outside the method, here is the definition of fuzzy affinity [24]. K is the input seed point, and the gray value of each pixel on the path is f(K).…”

Section: Improvement Of Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

Segmentation Algorithm Based on Neutrosophic Fuzzy C-Means Clustering and Its Application

2020

IJBDIT

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Image segmentation technology has become a key technology from image processing to image analysis. As the current image segmentation algorithms still have many defects, it is becoming more and more important to develop a suitable image segmentation algorithm. Although there are many achievements in this area, they all have shortcomings. In order to solve the shortcomings of traditional image segmentation algorithms, this article combines the neutrosophy image segmentation algorithm of LPG&PCA and improves the original FCM algorithm through in-depth research on the neutrosophy image segmentation method. After the experiment, it is found that when the improved algorithm is the lowest index function, the value of the original FCM algorithm is 0.8531, the value of the improved algorithm is 0.4751, and the categories of the original FCM algorithm and the improved algorithm are both 7, indicating the improved algorithm At the lowest exponential function, the correct clustering category can be obtained like the original FCM algorithm.

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Block-Matching Fuzzy C-Means clustering algorithm for segmentation of color images degraded with Gaussian noise

Cited by 25 publications

References 37 publications

Robust Gaussian‐base radial kernel fuzzy clustering algorithm for image segmentation

Robust Gaussian‐base radial kernel fuzzy clustering algorithm for image segmentation

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Segmentation Algorithm Based on Neutrosophic Fuzzy C-Means Clustering and Its Application

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