关键词: 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

Abstract: 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.

Key words: sparse representation, Principal Component Analysis（PCA） convolution kernel, image convolution, histogram, image classification