计算机与数字工程2017，Vol.45Issue(12)：2373-2378,6.DOI:10.3969/j.issn.1672-9722.2017.12.011
基于样本间最小欧氏距离的多特征融合识别算法研究
Research on Multi-feature Fusion Recognition Algorithm Based on Minimum Euclidean Distance Between Samples
摘要
Abstract
In the process of the target tracking,to improve the accuracy and real-time performance of target image recognition, this paper proposes a multi-feature fusion recognition method based on DS evidence theory and minimum Euclidean distance be?tween samples(E-DS).By image preprocessing and Sobel edge detection to target image,two types of visual features such as the tar?get color and geometry are extracted and normalized to form the target image feature vector;according to DS fusion theory,the mini?mum Euclidean distances between single samples are calculated and the results are used as evidences to construct the basic proba?bility assignment function,combined with DS combination rule,the final recognition results are given.The multi-feature E-DS fu?sion recognition method is applied to the target recognition test,the calculation results show that the average correct recognition rate of E-DS method reaches 95.49%,the highest recognition rate is 97.16%,and the variance of recognition rate between groups is mini?mum,which verifies the applicability of E-DS method.关键词
多特征融合识别/最小欧氏距离/DS证据融合理论Key words
multi-feature fusion recognition/minimum Euclidean distance/DS evidence fusion theory分类
信息技术与安全科学引用本文复制引用
刘如松..基于样本间最小欧氏距离的多特征融合识别算法研究[J].计算机与数字工程,2017,45(12):2373-2378,6.基金项目
国家自然科学基金项目(编号:61575155)资助. （编号:61575155）
