Robust semi-supervised spatial picture fuzzy clustering with local membership and KL-divergence for image segmentation

2021 ◽  
Author(s):  
Chengmao Wu ◽  
Jiajia Zhang
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Kl Divergence

Multiple Spatial Information Weighted Fuzzy Clustering for Image Segmentation

2020 ◽  
Author(s):  
Xiangdao Liu ◽  
Jin Zhou ◽  
Hui Jiang ◽  
C. L. Philip Chen ◽  
Tong Zhang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Spatial Information

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

Integrating guided filter into fuzzy clustering for noisy image segmentation

2018 ◽  
Vol 83 ◽  
pp. 235-248 ◽  
Author(s):  
Li Guo ◽  
Long Chen ◽  
C.L. Philip Chen ◽  
Jin Zhou
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Guided Filter ◽  
Noisy Image
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