摘要
提出了一种新的图像特征提取中选取最优小波分解树的方法.塔式小波分解对信号解不够全面,而小波包全分解又引入庞大的计算量,因此小波分解最优树的选取尤为重要.结合模糊c均值(FCM,Fuzzy C-Mean)聚类,提出了一种能同时进行小波自适应分解和纹理特征分类的纹理图像分割方法,该方法将无监督聚类中的聚类有效性参数引入到自适应小波分解的判决中,能根据无监督聚类分割的需要,自适应地选取小波包分解的树形结构和分解层数.相对于小波包全分解,节省了大量的运算,并能取得良好的分割效果.
A new method of optimal tree structure selection of wavelet transformation for image segmentation was presented. The standard pyramid-structure wavelet transform founded on the same recursive technique ： only the low-pass outputs were used. It could not adjust the decomposition to accurate and efficient texture description. Although the wavelet packet transform provided a much more detailed analysis of the frequency content of a texture, it is often the case that areas which contain little or no frequency information are recursively decomposed. So the selection of optimal wavelet basis for texture characterization is very important. By introducing the validity measure for fuzzy clustering to the decision of wavelet decomposition structure, the presented algorithm simultaneously performs the adaptive wavelet decomposition and the texture feature classification, moreover it adaptively chooses the wavelet decomposition structure and depth. Compared with the wavelet packet decomposition, the algorithm reduces the computational burden, while obtains satisfactory segmentation results.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2008年第5期572-575,共4页
Journal of Beijing University of Aeronautics and Astronautics
关键词
图像分割
小波变换
模糊C均值聚类
最优小波基
image segmentation
wavelet transform
fuzzy c-means clustering
optimal wavelet basis
