计算机工程2017，Vol.43Issue(5)：174-178,184,6.DOI:10.3969/j.issn.1000-3428.2017.05.028
基于均方差度量分块的自动加权稀疏表示算法
Automatic Weighting Sparse Representation Algorithm Based on Blocks by Mean Square Deviation
摘要
Abstract
Image segmentation algorithm ignores the image patch which contains the important local facial feature should have bigger weights of voting in the final classification.To solve this problem,this paper proposes an automatic weighting sparse representation algorithm.Through using sliding windows with overlapping,the mean square error value is calculated,and an automatic weighting strategy is given to measure the weight of each patch in the final classification.Experimental results on the public data set show that,compared with the commonly used classification algorithm and block algorithm,the proposed algorithm can get higher recognition rate in face recognition wherever it gives weights to the minimum error or the biggest votes.关键词
局部特征凸显/均方差度量/分块加权/稀疏表示/人脸识别Key words
local feature highlight/mean square deviation/block weighting/sparse representation/face recognition分类
信息技术与安全科学引用本文复制引用
魏明俊,许道云,徐梦珂..基于均方差度量分块的自动加权稀疏表示算法[J].计算机工程,2017,43(5):174-178,184,6.基金项目
国家自然科学基金(61262006,615400500) （61262006,615400500）
贵州省重大应用基础研究项目(黔科合JZ字[2014]2001号) （黔科合JZ字[2014]2001号）
贵州省科技厅联合基金(黔科合LH字[2014]7636号). （黔科合LH字[2014]7636号）
