基于均值/方差分类的三维SOM初始化模式库算法  

An initial pattern library algorithm based on mean/variance classification for 3D SOM

作　　者：程福林[1] 黎洪松[1] 

机构地区：[1]桂林电子科技大学信息与通信学院,广西桂林541004

出　　处：《桂林电子科技大学学报》2016年第1期35-38,共4页Journal of Guilin University of Electronic Technology

基　　金：国家自然科学基金(61261035);桂林电子科技大学研究生教育创新计划(GDYCSZ201451)

摘　　要：针对传统的初始化模式库算法存在模式矢量利用率低、与信源匹配程度不高的不足,提出一种基于均值/方差分类三维SOM初始化模式库算法。根据均值分类,将训练矢量集按照方差排序,以相同间隔抽取矢量组成初始化模式库。将算法运用到基于三维SOM算法的图像编码,结果表明,均值/方差分类初始化模式库算法无效模式矢量数量少、与信源匹配程度高,能有效地提高三维SOM算法的性能。The pattern vector utilization and source matching degree is low for the traditional initial pattern library algorithm, so a new initial pattern library algorithm based on mean/variance classification for 3D SOM is proposed. The training vectors are sorted by mean value, then the training vectors in each part are sorted by variance and initial pattern library is chosen in pattern vectors at the same intervals. Experimental results show that the initial pattern library algorithm based on mean/variance classification has less invalid pattern vectors and high source matching degree. It is an effective way to improve the performance of 3D SOM algorithm.

关 键 词：三维SOM 初始化模式库算法 图像模式识别 图像编码 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]