Multi-view fuzzy C-means clustering with kernel metric and local information for color image segmentation

IF 1.5 4区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xiumei Cai, Xi Yang, Chengmao Wu
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引用次数: 0

Abstract

Purpose

Multi-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.

Design/methodology/approach

The authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.

Findings

The authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.

Originality/value

Most of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.

利用核度量和局部信息进行多视角模糊 C-means 聚类,用于彩色图像分割
目的多视角模糊聚类算法在图像分割中的应用并不广泛,而且很多算法都缺乏鲁棒性。本文的目的是研究一种新的算法,这种算法可以在分割噪声图像时更好地分割图像,并尽可能多地保留图像的细节信息。首先,该算法引入了视图权重因子,可自动调整不同视图的权重,从而使每个视图都能获得最佳权重。其次,该算法加入了加权模糊因子,用于获取局部空间信息和局部灰度信息,以尽可能保留图像细节。最后,为了削弱噪声和异常值在图像分割中的影响,该算法采用了核距离度量,而不是欧氏距离。原创性/价值现有的多视图聚类算法大多是针对多视图数据集的,而多视图模糊聚类算法在处理噪声图像时无法消除噪声点和异常值。本文提出的算法具有更强的抗噪能力,能更好地保留原始图像的细节。
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来源期刊
Engineering Computations
Engineering Computations 工程技术-工程:综合
CiteScore
3.40
自引率
6.20%
发文量
61
审稿时长
5 months
期刊介绍: The journal presents its readers with broad coverage across all branches of engineering and science of the latest development and application of new solution algorithms, innovative numerical methods and/or solution techniques directed at the utilization of computational methods in engineering analysis, engineering design and practice. For more information visit: http://www.emeraldgrouppublishing.com/ec.htm
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