Research on Multi-feature Fusion Recognition Algorithm Based on Minimum Euclidean Distance Between Samples
LIU Rusong
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.
Keywords：multi-feature fusion recognitionminimum Euclidean distanceDS evidence fusion theory
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2373-2378 )