Food Image Segmentation Using an Improved Kernel Fuzzy C-Means Algorithm

2007 ◽  
Vol 50 (4) ◽  
pp. 1341-1348 ◽  
Author(s):  
C.-J. Du ◽  
D.-W. Sun
Keyword(s):  
Image Segmentation ◽  
Fuzzy C Means ◽  
Food Image ◽  
Fuzzy C Means Algorithm

scholarly journals A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

2011 ◽  
Vol 07 (01) ◽  
pp. 155-171 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Segmentation ◽  
Point Of View ◽  
Neutrosophic Set ◽  
Convergence Criterion ◽  
Complex Objects ◽  
Fuzzy C Means ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

Improved fuzzy C-means algorithm based on gray-level for image segmentation

2020 ◽  
Vol 35 (5) ◽  
pp. 499-507
Author(s):  
赵战民 ZHAO Zhan-min ◽  
朱占龙 ZHU Zhan-long ◽  
王军芬 WANG Jun-fen
Keyword(s):  
Image Segmentation ◽  
Gray Level ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Image segmentation by generalized hierarchical fuzzy C-means algorithm

2015 ◽  
Vol 28 (2) ◽  
pp. 961-973 ◽  
Author(s):  
Yuhui Zheng ◽  
Byeungwoo Jeon ◽  
Danhua Xu ◽  
Q.M. Jonathan Wu ◽  
Hui Zhang
Keyword(s):  
Image Segmentation ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm
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