首页|带PCA卷积的稀疏表示图像分类算法

带PCA卷积的稀疏表示图像分类算法

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
针对不同卷积核可以提取不同的图像特征,而卷积核的训练比较困难这一问题,提出一种带主成分分析(PCA)卷积的稀疏表示分类算法.先对训练样本集做分片去均值化处理,然后直接应用PCA算法提取所有分片的前K个特征向量作为卷积核,再用这些卷积核对原始图像进行卷积操作;并提出一种自动加权策略,对卷积处理后得到的K个特征图像进行加权叠加操作;最后对特征图像进行分块直方图统计稀疏化,并应用稀疏表示分类算法进行分类.在公共人脸数据集AR、CMU Multi-PIE、ORL以及数字手写体数据集MNIST上与常用分类算法进行对比实验,实验结果表明,带PCA卷积的稀疏表示分类算法具有更高的分类准确率.
Sparse representation image classification algorithm based on principal component analysis convolution
Different convolution kernels can obtain different image features, but training convolution kernels is difficult. To tackle this problem, an image classification algorithm based on the Principal Component Analysis(PCA)convolution and sparse representation is proposed. First, training samples are divided into small slices with mean-removed, then the PCA algorithm is directly applied to extract the first K eigenvectors as convolution kernels, then convolution operation is carried out for the original image and an automatic weighting strategy is proposed for integrating the image features obtained by convolution processing. Lastly, histogram statistics is used and the sparse representation algorithm is exploited for classification. Extensive experiments on representative face databases including AR, CMU Multi-PIE, ORL and digital handwriting dataset MNIST demonstrate that the proposed algorithm can get better recognition than state-of-the-art methods.

sparse representationPrincipal Component Analysis(PCA)convolution kernelimage convolutionhisto-gramimage classification

魏明俊、许道云、徐梦珂

展开 >

贵州大学 计算机科学与技术学院,贵阳 550025

贵州大学 理学院,贵阳 550025

稀疏表示 主成分分析卷积核 图像卷积 直方图统计 图像分类

国家自然科学基金国家自然科学基金贵州省重大应用基础研究项目贵州省科技厅联合基金

6126200661540050黔科合JZ字[2014]2001号黔科合LH字[2014]7636号

2017

10.3778/j.issn.1002-8331.1612-0407

计算机工程与应用
华北计算技术研究所

计算机工程与应用

CSTPCDCSCD北大核心
影响因子：0.683
ISSN：1002-8331
年,卷(期)：2017.53(14)
  • 5
  • 21