[1]康家银,张文娟.用于图像分割的非局部空间约束的核FCM算法[J].南京师范大学学报(自然科学版),2019,42(03):122-128.[doi:10.3969/j.issn.1001-4616.2019.03.016]
 Kang Jiayin,Zhang Wenjuan.Kernelized FCM Algorithm with Non-Local SpatialConstraint for Image Segmentation[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):122-128.[doi:10.3969/j.issn.1001-4616.2019.03.016]
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用于图像分割的非局部空间约束的核FCM算法()
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《南京师范大学学报》(自然科学版)[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年03期
页码:
122-128
栏目:
·全国机器学习会议论文专栏·
出版日期:
2019-09-30

文章信息/Info

Title:
Kernelized FCM Algorithm with Non-Local SpatialConstraint for Image Segmentation
文章编号:
1001-4616(2019)03-0122-07
作者:
康家银1张文娟2
(1.淮海工学院电子工程学院,江苏 连云港 222005)(2.淮海工学院计算机工程学院,江苏 连云港 222005)
Author(s):
Kang Jiayin1Zhang 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)
关键词:
图像分割模糊C均值模糊聚类核方法空间约束
Keywords:
image segmentationfuzzy c-meansfuzzy clusteringkernel methodspatial constraint
分类号:
TP391.4
DOI:
10.3969/j.issn.1001-4616.2019.03.016
文献标志码:
A
摘要:
图像分割是图像分析、图像理解的前提和关键,其结果直接决定着图像分析和理解的质量. 模糊C均值(Fuzzy C-Means,FCM)聚类算法是一种常用的图像分割算法. 然而,由于经典的FCM算法只考虑像素自身,从而对外围噪声比较敏感. 因此,提出了一种改进的用于图像分割的FCM聚类算法. 该算法通过利用核方法修改FCA-NLASC算法中的目标函数而实现,即用核距离替代FCA-NLASC中的欧氏距离,相应地得到核FCA-NLASC聚类算法——KNLASC-FCM聚类算法. 利用提出的算法分别进行人工合成图像和实际图像的实验结果表明,当图像含有噪声时,与算法FCA-NLASC相比,KNLASC-FCM算法在主观视觉、客观量化两方面的评价中均具有更好的分割性能.
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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-07-05.基金项目:国家自然科学基金(61601194)、江苏省高等学校自然科学研究项目(17KJB520003). 通讯联系人:康家银,副教授,研究方向:图像处理、机器学习. E-mail:kangjiayin2002@163.com
更新日期/Last Update: 2019-09-30