摘要
本文提出了一种利用特征加权进行基于小波变换的纹理分类方法。本方法选用Daubechies正交小波,采用标准的金字塔结构小波变换,将小波变换各个频带输出的l_1范数作为纹理分类的特征,并根据特征本身的离散程度对其进行加权,最后,采用最小距离分类器进行分类。对近千例测试样本的分类实验表明,本文提出的算法与无特征加权算法相比,性能有明显的提高。
In this paper, a new approach to wavelet transform-based texture classification using feature weighting is presented. The Daubechies orthogonal wavelets are selected and the standard pyramid-structured wavelet transform is employed. This approach extracts the l1 -norm for each frequency channel of the wavelet transform output as the features for texture classification, and weights these features according to their own degree of dispersion. A minimum distance classifier is used in the classification procedure. The classification experiments for almost 1000 testing samples obtained from 20 classes of textures show that the performance of the presented texture classification algorithm is much better than the algorithm without feature weighting.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
1999年第3期262-267,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金
863计划
智能技术与系统国家重点实验室资助项目
关键词
纹理分析
纹理分类
小波变换
特征提取
图像分析
Texture Analysis, Texture Classification, Wavelet Transform, Feature Extraction
