[an error occurred while processing this directive]
   
 
���¿��ټ��� �߼�����
   ��ҳ  �ڿ�����  ��ί��  Ͷ��ָ��  �ڿ�����  ��������  �� �� ��  ��ϵ����
�������պ����ѧѧ�� 2008, Vol. 34 Issue (03) :267-270    DOI:
���� ����Ŀ¼ | ����Ŀ¼ | ������� | �߼����� << | >>
�˿ռ������ͼ����������еļ��㷨
Ԭ����,����,�ɹ�*
�������պ����ѧ ������Ϣ����ѧԺ, ���� 100083
Simplified method of kernel fuzzy c-means clustering for image texture classification
Yuan Yunneng, Wu Yang, Cheng Gong*
School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

ժҪ
�����
�������
Download: PDF (0KB)   HTML 1KB   Export: BibTeX or EndNote (RIS)      Supporting Info
ժҪ ģ��c��ֵ�����ѹ㷺Ӧ����ģ��ģʽʶ������,���������Բ��ɷ����ݲ�������.�ں˷�����ͨ�����������ݾ���������ӳ��ͶӰ����ά�����ռ�����������Է��������.����ͳ��ģ��c��ֵ�����㷨Ӧ���ں˿ռ���,�����Բ��ɷֵ����������˺˿ռ����ķ���ʵ��,�õ�����ȷ�ķ�����.����ͼ������з�������(��Ӧͼ������)��Ŀ�Ӵ�,����˺˿ռ�����㷨����������ļ���������.���,�ں˿ռ����Ļ�����,����˶�ͼ���Ƚ��й��ָ�,�ٶԹ��ָ��ͼ�����к˿ռ����ķ���,��󽵵��˸�ά�ռ�����������������ɱ�,��ȡ�������õķ���Ч��.
Service
�ѱ����Ƽ�������
�����ҵ����
�������ù�����
Email Alert
RSS
�����������
Ԭ����
����
�ɹ�
�ؼ����� ͼ��ָ�   �������   �˷���   ģ��c��ֵ����     
Abstract�� The fuzzy c-means clustering algorithm is a widely applied method for acquiring fuzzy pattern from data, but it is not suitable for the clustering of linear inseparable data. In mercer kernel method, the problem of nonlinear separability of classes can be tricked by projecting the input data to a higher dimensional feature space in a nonlinear manner. So the fuzzy c-means clustering method was used in the mercer kernel space. The classification experiment illustrated that the kernel fuzzy c-means clustering (KFCM) algorithm was suitable for the clustering of linear inseparable data. When KFCM clustering was used in image segmentation, the large number of classification samples always caused the computational burden. The image classification procedure was divided into two steps: firstly, the image was over-segmented into large numbers of small regions according to the input features; secondly, they were classified with KFCM. The computational burden was reduced by the decrease of classification samples, while the classification result was almost as good as KFCM-s.
Keywords�� image segmentation   texture classification   kernel method   fuzzy c-means clustering     
Received 2007-06-29;
Fund:

�����ص�ʵ���һ���������Ŀ

About author: Ԭ����(1962-),��,���������,������,yuan203@buaa.edu.cn.
���ñ���:   
Ԭ����,����,�ɹ�.�˿ռ������ͼ����������еļ��㷨[J]  �������պ����ѧѧ��, 2008,V34(03): 267-270
Yuan Yunneng, Wu Yang, Cheng Gong.Simplified method of kernel fuzzy c-means clustering for image texture classification[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2008,V34(03): 267-270
���ӱ���:  
http://bhxb.buaa.edu.cn//CN/     ��     http://bhxb.buaa.edu.cn//CN/Y2008/V34/I03/267
Copyright 2010 by �������պ����ѧѧ��