A new feature fusion method at decision level and its application  

作　　者：韩光 赵春霞 张浩峰 袁夏 

机构地区：[1]College of Computer Science and Technology,Nanjing University of Science and Technology

出　　处：《Optoelectronics Letters》2010年第2期129-132,共4页光电子快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Nos.60705020 and 90820306)

摘　　要：To solve the problem that using only one feature is poor for the terrestrial environment classification,a new feature fusion method at decision level is proposed in this paper.An eigenvector is obtained by using a Gaussian mixture model(GMM) firstly,and the probabilities of the eigenvector belonging to a certain class of the texture and color are computed.Then the probabilities are multiplied by different weights according to the contribution,and summed to get the maximal likelihood probability to achieve feature fusion.Experimental results demonstrate that the method in this paper is better than other current methods and the classification performance is superior to a single feature obviously.To solve the problem that using only one feature is poor for the terrestrial environment classification, a new feature fusion method at decision level is proposed in this paper. An eigenvector is obtained by using a Gaussian mixture model （GMM） firstly, and the probabilities of the eigenvector belonging to a certain class of the texture and color are computed. Then the probabilities are multiplied by different weights according to the contribution, and summed to get the maximal likelihood probability to achieve feature fusion. Experimental results demonstrate that the method in this paper is better than other current methods and the classification performance is superior to a single feature obviously.

关 键 词：融合方法 决策层 应用 高斯混合模型 特征向量 概率计算 环境分类 GMM 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]