| 刘金平,唐朝晖,徐鹏飞,陈.基于B样条概率密度估计的纹理图像分类[J].光电子激光,2017,28(5):538~546 |
| 基于B样条概率密度估计的纹理图像分类 |
| On texture mage classification via B-spline probability density function estim ation |
| 投稿时间：2016-03-05 |
| DOI： |
| 中文关键词: B样条概率密度估计 纹理图像分类 各向异性高斯核 最远邻聚类 图像统计建模 |
| 英文关键词:B-spline probability density estimation texture image classificatio n anisotrophic Gaussian kernel furthest-neighbor clustering image statistica l modeling |
| 基金项目:国家自然科学基金(61501183,4)和湖南省科技计划基金(2013FJ4051)资助项目 (1.湖南师范大学 数学与计算机科学学院,湖南 长沙 410081； 2.中南大学 信息科学与工程学院,湖南 长沙 410083； 3.湖南工业大学 计算机与通信学院,湖南 株洲 412007) |
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| 中文摘要: |
| 提出一种基于B样条概率密度函数(PDF,probability density function)估计的复杂纹 理图像分类识别方法,主要包括图像纹理PDF特征学习、 表征以及纹理类型识别3个步骤。在图像纹理PDF特征学习和表征中,引入各向异性高斯导 数方向滤波器获得纹理图像多尺度和 多方向空间结构；然后基于预先固定的B样条基函数,将图像空间结构PDF估计转化为与 基函数相对应的权值向量估计；之 后采用最远邻聚类方法,获得图像空间纹理结构的PDF特征字典库；最后采用最近邻方法, 获得各类纹理在特征字典库上的直方图 分布表示。在纹理类型识别阶段,基于直方图距离测量结果实现纹理图像分类识别。在不同 纹理图像数据库上进行了大量的验证性和对比性实验,实验结果表明所提方法的有效性 和优越性。 |
| 英文摘要: |
| A B-spline probability density function (PDF) estimation-based texture image identification method is presented in this work, which can be categorized into three processing phases,including PDF feature dictionary learn ing phase,PDF feature representation phase and texture identification phase.In the PDF feature dictionary learning and feature represe ntation phases,anisotropic Gaussian derivative filters are adopted to achieve the image′s spatial textural structures under various Gaussi an observation scales and orientations in advance.Next,the non-parametric PDF feature of the local spatial-structural texture is transfor med to parametric weighting vector estimation based on the pre-fixed B-spline basis functions.The PDF feature dictionary base of the tex ture image samples is obtained by the furthest-neighbor clustering method,and the nearest neighbor method is used successively to obtain the histogram distributions for all kinds of image texture categories for the following texture identification.Finally,the image texture is identified by the simple histogram distance measurement. Abundant confirmative and comparative tests on the commonly-used testing sets i ndicate the effectiveness and superiority of the proposed method . |
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