基于统计几何的纹理特征提取及分类算法

Image feature extraction and classification based on statistical geometrical features

纹理识别是计算机视觉领域一个重要的课题,本文研究了统计几何特征(SGF)纹理分析方法并与向量机结合构建分类系统。对支持向量机(SVM)的多分类方法的实现,构建了粗分类和细分类相结合的多分类器,实现了纹理图像的准确划分,为有效纹理特征的表示奠定了基础。本文对统计几何特征提取方法进行了研究,利用图像函数图来进行纹理描述,使用一个可变的阈值把一幅灰度纹理图像切割成一系列二进制图像,由二进制图像的连通域、几何拓扑属性推导纹理描述特征。实验结果表明,统计几何特征具有非常强的纹理描述能力,同时能够克服图像的旋转。

Texture recognition is an important topic in the field of computer vision, statistical geometrical features （SGF） is especially studied in the paper,and the texture classification system is constructed by combining these methods with support vector machines（SVM）.In this paper,multiple classifier based on SVM was studied to built the raw classification system and subclass system,which can achieve highly accurate texture classification.As a new feature extraction method,SGF describes the texture with image function.The gray image is split into a series of binary images with variable thresholds.The texture description feature can be deduced by the connected domain and geometric topology property of binary images.Texture classification experiments result shows that SGF has very strong ability in texture description and rotation overcoming.