基于模糊核聚类的图像SVM分类辨识  

Image Classification and Identification through SVM Based on Fuzzy Kernel Clustering

作　　者：于文勇[1] 康晓东[1] 葛文杰[2] 王昊[1] 

机构地区：[1]天津医科大学医学影像学院,天津300070 [2]河北工业大学电子信息工程学院,天津300130

出　　处：《计算机科学》2015年第3期307-310,320,共5页Computer Science

摘　　要：提出一种结合特征场和模糊核聚类支持向量机的图像分类辨识方法。首先,构造符合人类视觉特性的图像彩色和纹理特征数据场,一方面,引入新阈值,建立图像纹理特征;另一方面,在图像彩色特征上,对能够引起注意的像素区域的像素点进行加权处理,并使用彩色空间分布离散度来描述彩色的空间分布。其次,采用模糊核聚类支持向量机对图像进行分类研究。在使用特征空间时,不仅考虑了样本与类中心间的关系,还考虑了类中各个样本间的关系,以模糊连接度来度量类中各个样本间的关系,并以二叉树方式构造子分类器。实验结果表明,该方法可以获得较好的图像分类效果。A method of image classification and identification combined with characteristic field and SVM based on fuzzy kernel clustering was proposed in this paper.First,the structure corresponds to image color of human visual characteristics and data field of texture.For one thing,the new threshold is introduced and the image texture is established.For another,attractive pixel to pixel area is weighted and processed,and the spatial distribution of color is described by using dispersion of color spatial distribution.Second,SVM based on fuzzy kernel clustering is adopted to study a classification of image identification.On the feature space,not only the relationship of samples between its cluster centers but also the each samples are all taken into account.The relation between each samples in the cluster is measured based on fuzzy connectedness and a binary tree classifier is constructed.Experimental results show that this method can achieve a better effect of image classification.

关 键 词：支持向量机 隶属度函数 模糊核聚类 数据场 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]