Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms

Raja Kishor Duggirala
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引用次数: 4

Abstract

Image segmentation is an essential technique of image processing for analyzing an image by partitioning it into non-overlapped regions each region referring to a set of pixels. Image segmentation approaches can be divided into four categories. They are thresholding, edge detection, region extraction and clustering. Clustering techniques can be used for partitioning datasets into groups according to the homogeneity of data points. The present research work proposes two algorithms involving hybridization of K-Means ( KM ) and Fuzzy C-Means ( FCM ) techniques as an attempt to achieve better clustering results. Along with the proposed hybrid algorithms, the present work also experiments with the standard K-Means and FCM algorithms. All the algorithms are experimented on four images. CPU Time, clustering fitness and sum of squared errors (SSE) are computed for measuring clustering performance of the algorithms. In all the experiments it is observed that the proposed hybrid algorithm KMandFCM is consistently producing better clustering results.
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基于k -均值和模糊c -均值混合算法的图像分割
图像分割是图像处理的一项基本技术,通过将图像划分为不重叠的区域来分析图像,每个区域指的是一组像素。图像分割方法可以分为四类。它们是阈值分割、边缘检测、区域提取和聚类。聚类技术可用于根据数据点的同质性将数据集划分为组。本研究提出了两种混合K-Means (KM)和模糊C-Means (FCM)技术的算法,试图获得更好的聚类结果。除了提出的混合算法外,本工作还对标准K-Means和FCM算法进行了实验。所有算法都在四幅图像上进行了实验。计算CPU时间、聚类适应度和误差平方和(SSE)来衡量算法的聚类性能。在所有的实验中,观察到所提出的混合算法KMandFCM始终产生较好的聚类结果。
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