|Table of Contents|

Improved fast fuzzy C-means clustering algorithm for image segmentation

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2016年03期
Page:
309-
Research Field:
Publishing date:

Info

Title:
Improved fast fuzzy C-means clustering algorithm for image segmentation
Author(s):
Xu Qin1Tang Dunbing2Cai Qixiang2
1.College of Electrical and Electronic Engineering,Anhui Science and Technology University, Chuzhou 233100,China; 2.College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
Keywords:
fuzzy clustering C-means clustering image segmentation pixel space gray histogram feature space curve fitting method clustering number initial clustering center
PACS:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2016.40.03.010
Abstract:
In order to improve the speed of image segmentation,an improved fast image segmentation algorithm is proposed based on the application of conventional fuzzy C-means(FCM)clustering algorithm for image automatic segmentation.An image is mapped to the corresponding gray histogram feature space from the pixel space.Data clustering analysis is realized in the feature space and the amount of the clustering sample is reduced.According to the characteristics of the gray histogram,the clustering number and the initial clustering center are obtained by curve fitting method.The experimental results show that the iterative number is reduced by 10% and the run time is reduced by 6% and effective image segmentation is realized.

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Last Update: 2016-06-30