基于NIB2DPCA的彩色图像过完整分块特征抽取方法  

NIB2DPCA-based Color Image Feature Extraction Method with Over-complete Divided

作　　者：黄可望[1] 冯宗越 朱嘉钢[2,3] 

机构地区：[1]无锡职业技术学院物联网技术学院,江苏无锡214121 [2]江南大学物联网应用技术教育部工程研究中心,江苏无锡214122 [3]江南大学晓山股份联合实验室,江苏无锡214122

出　　处：《计算机与现代化》2015年第12期78-83,89,共7页Computer and Modernization

基　　金：江苏省自然科学基金资助项目(BK20131106);江苏省产学研联创项目(BY2013015-40)

摘　　要：在小空间占用的快速彩色图像的特征抽取方法和模块化FPCA(M-FPCA)彩色图像特征提取方法的基础上,结合最新的过完整表示思想,提出基于NIB2DPCA的彩色图像过完整分块特征抽取新方法。该方法对彩色图像进行过完整分块,然后对子图像模块从R、G、B三个信道用NIB2DPCA方法进行特征提取、重构,并进行多模块融合,最终获得分类的特征矩阵。该方法提取的信息量远大于原图像,提高了彩色图像的识别率。通过在FEI和CVL标准彩色人脸数据库上的对比实验表明,所提出方法的人脸识别准确率比文献[14]中的小空间占用的快速彩色图像特征抽取方法提高约4%,比文献[19]中的彩色图像M-FPCA方法提高约5%。Based on color image feature extraction method with small space occupying and fast speed and color image feature ex- traction method with Modular Factored Principal Components Analysis （color M-FPCA）, combined with the latest over-complete representation idea, a novel method named NIB2DPCA-based color image feature extraction method with over-complete divided was proposed. This method can make the color image over-complete divided into smaller modular images, then NIB2DPCA is employed to extract feature information from three channels of a given sub color image module respectively. Then the three extracted feature matrices are reconstructed and multi module integrated. Finally, the classification feature matrix is gotten. The informa- tion amount extracted by the novel method is much larger than the original color image by this method and it improves the recogni- tion accuracy of color image. The results of contrast experiments on CVL and FEI color face databases show that, the proposed method can obtain a higher accuracy by about 4% than color image feature extraction method with small space occupying and fast speed in the reference[ 14] and also obtain a higher accuracy by about 5% than color M-FPCA in the reference[ 19].

关 键 词：彩色人脸识别 过完整分块 NIB2DPCA 特征抽取 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]