|Table of Contents|

Kernelized FCM Algorithm with Non-Local SpatialConstraint for Image Segmentation(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年03期
Page:
122-128
Research Field:
·全国机器学习会议论文专栏·
Publishing date:

Info

Title:
Kernelized FCM Algorithm with Non-Local SpatialConstraint for Image Segmentation
Author(s):
Kang Jiayin1; Zhang Wenjuan2
(1.School of Electronics Engineering,Huaihai Institute of Technology,Lianyungang 222005,China)(2.School of Computer Engineering,Huaihai Institute of Technology,Lianyungang 222005,China)
Keywords:
image segmentation; fuzzy c-means; fuzzy clustering; kernel method; spatial constraint
PACS:
TP391.4
DOI:
10.3969/j.issn.1001-4616.2019.03.016
Abstract:
Image segmentation is the crucial and first step of image analysis and understanding,and the segmenting result directly determines the quality of image analysis and understanding. Fuzzy C-means(FCM)is a commonly used algorithm for image segmentation. However,conventional FCM algorithm is sensitive to noise due to not taking neighboring pixels into consideration. To this end,a modified FCM clustering algorithm for image segmentation is proposed in this paper. The algorithm is realized by modifying the objective function in the FCA-NLASC algorithm using kernel method,i.e.,the original Euclidean distance in the FCA-NLASC is replaced by a kernel-induced distance,and thus corresponding algorithm is derived and called as the kernelized FCA-NLASC,shorted in KNLASC-FCM clustering algorithm. Experimental results on both artificially synthesized image and real image demonstrate that when images are contaminated by noises,the KNLASC-FCM algorithm has better segmentation performance than FCA-NLASC both in visually subjective and quantitatively objective evaluations.

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Last Update: 2019-09-30