Kernel generalized fuzzy c-means clustering with spatial information for image segmentation

2013 ◽  
Vol 23 (1) ◽  
pp. 184-199 ◽  
Author(s):  
Feng Zhao ◽  
Licheng Jiao ◽  
Hanqiang Liu
Author(s):  
Guang Hu ◽  
Zhenbin Du

In order to resolve the disadvantages of fuzzy C-means (FCM) clustering algorithm for image segmentation, an improved Kernel-based fuzzy C-means (KFCM) clustering algorithm is proposed. First, the reason why the kernel function is introduced is researched on the basis of the classical KFCM clustering. Then, using spatial neighborhood constraint property of image pixels, an adaptive weighted coefficient is introduced into KFCM to control the influence of the neighborhood pixels to the central pixel automatically. At last, a judging rule for partition fuzzy clustering numbers is proposed that can decide the best clustering partition numbers and provide an optimization foundation for clustering algorithm. An adaptive kernel-based fuzzy C-means clustering with spatial constraints (AKFCMS) model for image segmentation approach is proposed in order to improve the efficiency of image segmentation. Various experiment results show that the proposed approach can get the spatial information features of an image accurately and is robust to realize image segmentation.


2013 ◽  
Vol 712-715 ◽  
pp. 2349-2353
Author(s):  
Hong Lan ◽  
Shao Bin Jin

Fuzzy C-Means clustering(FCM) algorithm plays an important role in image segmentation, but it is sensitive to noise because of not taking into account the spatial information. Addressing this problem, this paper presents an improved suppressed FCM algorithm based on the pixels and the spatial neighborhood information of the image. The algorithm combines the two-dimentional histogram and suppressed FCM algorithm together. First, construct a two-dimentional histogram instead of one-dimentional histogram, which can better distinguish the distribution of the object and background for noisy images. Then determine the initial clustering based on two-dimensional histogram. Last, provide a new way to determine the suppressed factor and use the improved FCM algorithm to realize the image segmentation. Experimental results show that the improved algorithm is effective to improve the clustering speed, and can achieve better segmentation results.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 95681-95697
Author(s):  
Xiaolei Zhang ◽  
Weijun Pan ◽  
Zhengyuan Wu ◽  
Jiayang Chen ◽  
Yifei Mao ◽  
...  

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