基于Kmeans-GMM模型的地板块纹理分类算法  

Plate Texture Classification Algorithm Based on Kmeans-GMM Model

作　　者：陈宇[1] 王爱斐[1] 江露[1] 曹军[1] 

机构地区：[1]东北林业大学信息与计算机学院,黑龙江哈尔滨150040

出　　处：《哈尔滨理工大学学报》2013年第4期69-73,共5页Journal of Harbin University of Science and Technology

基　　金：国家948项目(2011-04-04);中央高校基本科研业务费专项资金项目(DL12CB02);黑龙江省教育厅科学技术研究项目(12513016);教育部大学生创新训练计划(201210225060)

摘　　要：为解决地板块纹理分类难度大的问题,提出了一种基于Kmeans-GMM模型的地板块纹理分类方法.在阐述混合高斯模型GMM及参数估计算法原理的基础上,采用灰度共生矩阵提取地板块纹理特征,结合Kmeans算法,通过训练得到各类纹理所对应的混合高斯模型GMM的参数,实现对地板块纹理分类.实验结果表明该方法辨识准确率高、识别速度快,优于传统的神经网络分类法以及SVM算法,为地板块纹理分类的研究提供了一个新的思路.In order to solve the problem of difficulty in plate texture classification, the paper proposes the plate texture classification algorithm based on Kmeans-GMM model. Based on the Gaussian mixture model （GMM） and the principle of parameter estimation algorithm, this paper adopts the gray level co-occurrence matrix to extract plate texture feature, and estimates the parameters about the Gaussian mixture model （GMM） combining with Kmeans algorithm through training to realize the plate texture classification. The experimental results show that the algorithm has the advantages of faster speed and higher accuracy of recognition than the traditional neural network algorithm and SVM algorithm. The algorithm provides a new thought for the research on the plate texture classifica- tion algorithm.

关 键 词：纹理识别 混合高斯模型 EM Kmeans 

分 类 号：TN917[电子电信—通信与信息系统]