摘要
传统鲁棒差分盒计数法(RDBC)已成功用于高斯噪声图像的分形维估计,但由于对椒盐噪声较敏感,因此不再适用于椒盐噪声图像的分形维估计和图像分类。本文提出一种基于中值绝对偏差(MAD)的分形维数计算方法(MAD-DBC)。该方法利用MAD进行差分盒计数,对椒盐噪声具有很好的鲁棒性特点。实验结果表明,利用小波多分辨率的DBC、RDBC和MAD-DBC对椒盐噪声的16种Brodatz纹理图像进行分类,MAD-DBC具有更高的识别率和更好的噪声鲁棒性。
The traditional robust differential box-counting method （ RDBC） has been successfully used for calculating fractal di-mension of an image degraded by Gaussian noise .However , it is not suitable for estimating fractal dimension of salt ＆ pepper noisy images and classifying those images .This paper presents a MAD-based method （ MAD-DBC） for calculating fractal dimen-sion of an image .The method uses MAD for differential box-counting , which is robust against salt＆pepper noises .Classification experiments on Brodatz texture images show that , compared with DBC and RDBC , the MAD-DBC achieves higher classification rate and better noise robustness .
出处
《计算机与现代化》
2013年第12期102-105,共4页
Computer and Modernization
基金
国家自然科学基金资助项目(61003178
11201312
11071150)
深圳科技计划基础研究项目(JC201105170615A)
关键词
分形维
差分盒计数法
MAD
图像分类
fractal dimension
differential box-counting
MAD
image classification
