摘要
针对多纹理图像较难准确分割的问题,提出了基于小波包框架子带互补特征提取的纹理分割方法.该方法利用小波包框架对原始纹理图像进行分解,对所得子带系数在每个像素的邻域窗口提取两类特征——平均绝对偏差及子带系数梯度方向直方图的均值与标准差,并利用改进的空间模糊c均值聚类方法对纹理像素进行聚类.由于此方法考虑了像素特征值局部标准差的空间分布,因此所得分割结果在纹理边界处的像素误分类率较低.以多幅Brodatz纹理图像进行相应的实验对比测试.Fisher线性判别分析实验显示,两类特征的组合比单一特征具有更强的纹理区别能力;纹理分割实验表明,文中设计的分割方案能实现较高的分割准确率;算法运行速度测试表明,文中方法是实用的.
As multi-texture images are difficult to segment accurately,this paper proposes a texture segmentation method based on the complementary feature extraction of wavelet packet frame sub-band.In this method,first,the original texture image is decomposed by using the wavelet packet frame.Then,two sets of features in the neighborhood window of each pixel,namely,the average absolute deviation of sub-band coefficients and the mean and standard deviation of the histogram of oriented gradients for sub-band coefficients,are extracted for each sub-band coefficient.Moreover,texture pixels are clustered via the improved spatial fuzzy c-means clustering.As the proposed method takes into consideration the spatial distribution of the local standard deviation of pixel feature values,it helps to obtain texture segmentation results with low misclassification rate of pixels near the texture boundary.Finally,several texture images from Brodatz album are used to test the proposed method.Fisher linear discriminant analysis indicates that the combination of the two sets of features is more effective in texture distinction than any single feature.Texture segmentation experiments show that the proposed segmentation scheme helps to achieve higher segmentation accuracy.In addition,test results of algorithm speed prove that the proposed method is applicable.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第12期56-62,共7页
Journal of South China University of Technology(Natural Science Edition)
基金
广东省科技攻关项目(2008B01040004)
广东省医学科研基金资助项目(A2011218)
关键词
纹理分割
小波包框架
梯度方向直方图
空间模糊c均值聚类
texture segmentation
wavelet packet frame
histogram of oriented gradients
spatial fuzzy c-means clustering
