稀疏表示分类中遮挡字典构造方法的改进

An Improvement of Method to Construct Occlusion Dictionary of Sparse Representation based Classification

针对稀疏表示分类算法中遮挡字典维数高且无冗余的问题,提出一种遮挡字典构造方法.首先通过图像分块得到各级的遮挡基图像;然后将所有互不相同的遮挡基图像按字典顺序转化为向量,并用这些向量作为遮挡字典的列,从而构造出维数相对较低且具有一定冗余度的遮挡字典.实验结果表明,该方法不仅明显提高了稀疏表示分类算法对遮挡人脸的识别率,而且还能通过减少图像的分块级数降低稀疏分解的耗时量,提高运算效率.

The occlusion dictionary of sparse representation based classification algorithm is a high‐dimensional dictionary without redundancy .To solve this problem ,a method to construct occlusion dictionary is proposed in this paper .First ,the input image is divided into small blocks and occlusion base images at all levels are attained .Then each unique occlusion base images is expanded into column vectors in dictionary order . Finally , a low‐dimensional occlusion dictionary with redundancy is constructed by putting these column vectors together . The experimental results show that the proposed method not only obviously improves the occluded face recognition rate ,but also saves sparse decomposition time and improves running efficiency of sparse representation based classification algorithm by reducing the blocking partition level of image .