A Fuzzy Clustering Algorithm of Automatic Classification Based on EnFCM

2014 ◽  
Vol 989-994 ◽  
pp. 1489-1492 ◽  
Author(s):  
Hong Wei Han ◽  
Lin Tian ◽  
Jia Qing Miao
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Automatic Classification ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Algorithm ◽  
Detection Technology ◽  
Fuzzy C Means Clustering ◽  
Point Detection

Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm for image segmentation, and has been widely applied because the segmentation results are consistent with human visual characteristics. Enhanced fuzzy c-means clustering (EnFCM) algorithm is the improved FCM algorithm, which reduces the computational complexity. But, both FCM algorithm and EnFCM algorithm, clustering number still need to be manually determined. This paper, in order to realize the automation degree of algorithm, presents an improved algorithm. It first analyzes the histogram, then automatically determines the clustering number and peak value of each class through use of the peak point detection technology, finally segments image by using EnFCM algorithm. Experiments show that this method is a kind of faster fuzzy clustering algorithm with automatic classification ability for image segmentation.

Application of Hard C-Means and Fuzzy C-Means in Data Fusion

2012 ◽  
Vol 190-191 ◽  
pp. 265-268
Author(s):  
Ai Hong Tang ◽  
Lian Cai ◽  
You Mei Zhang
Keyword(s):  
Data Fusion ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Large Data ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Data Similarity ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy C Means Algorithm

This article describes two kinds of Fuzzy clustering algorithm based on partition,Fuzzy C-means algorithm is on the basis of the hard C-means algorithm, and get a big improvement, making large data similarity as far as possible together. As a result of Simulation, FCM algorithm has more reasonable than HCM method on convergence, data fusion, and so on.

scholarly journals Robust FCM Algorithm with Local and Gray Information for Image Segmentation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hanane Barrah ◽  
Abdeljabbar Cherkaoui ◽  
Driss Sarsri
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Gray Level ◽  
Fcm Algorithm ◽  
Fuzzy Clustering Algorithm ◽  
Noise Type ◽  
Level Information ◽  
Almost All ◽  
Segmentation Problem

The FCM (fuzzy c-mean) algorithm has been extended and modified in many ways in order to solve the image segmentation problem. However, almost all the extensions require the adjustment of at least one parameter that depends on the image itself. To overcome this problem and provide a robust fuzzy clustering algorithm that is fully free of the empirical parameters and noise type-independent, we propose a new factor that includes the local spatial and the gray level information. Actually, this work provides three extensions of the FCM algorithm that proved their efficiency on synthetic and real images.

FUZZY C-MEANS CLUSTERING ALGORITHM WITH TWO LAYERS AND ITS APPLICATION TO IMAGE SEGMENTATION BASED ON TWO-DIMENSIONAL HISTOGRAM

1994 ◽  
Vol 02 (03) ◽  
pp. 343-350 ◽  
Author(s):  
WEIXIN XIE ◽  
JIANZHUANG LIU
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Two Dimensional ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Hard Clustering

This paper presents a fast fuzzy c-means (FCM) clustering algorithm with two layers, which is a mergence of hard clustering and fuzzy clustering. The result of hard clustering is used to initialize the c cluster centers in fuzzy clustering, and then the number of iteration steps is reduced. The application of the proposed algorithm to image segmentation based on the two dimensional histogram is provided to show its computational efficience.

scholarly journals Image Segmentation Based on Intuitionistic Type-2 FCM Algorithm

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Zhongqiang Pan ◽  
Xiangjian Chen
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Experimental Results ◽  
Fcm Algorithm ◽  
Hybrid Clustering ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Clustering Algorithm ◽  
Segmentation Accuracy

Due to using the fuzzy clustering algorithm, the accuracy of image segmentation is nothigh enough. So one hybrid clustering algorithm combined with intuitionistic fuzzy factor and localspatial information is proposed. Experimental results show that the proposed algorithm is superiorto other methods in image segmentation accuracy and improves the robustness of the algorithm.

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