Kernel based Sparse Representation Classifier (KSRC) can classify images with acceptable performance. In addition, Multiple Kernel Learning based SRC (MKL-SRC) computes the weighted sum of multiple kernels in order to construct a unified kernel while the weight of each kernel is calculated as a fixed value in the training phase. In this paper, an MKL-SRC with non-fixed kernel weights for dictionary atoms is proposed. Kernel weights are embedded as new variables to the main KSRC goal function and the resulted optimization problem is solved to find the sparse coefficients and kernel weights simultaneously. As a result, an atom specific multiple kernel dictionary is computed in the training phase which is used by SRC to classify test images. Also, it is proved that the resulting optimization problem is convex and is solvable via common algorithms. The experimental results demonstrate the effectiveness of the proposed approach.

基于内核的稀疏表示分类器 (KSRC) 可以对具有可接受性能的图像进行分类。此外，基于多核学习的 SRC (MKL-SRC) 计算多个核的加权和以构建统一的核，而每个核的权重在训练阶段计算为固定值。在本文中，提出了一种具有非固定核权重的字典原子的 MKL-SRC。内核权重作为新变量嵌入到主 KSRC 目标函数中，并且解决了由此产生的优化问题以同时找到稀疏系数和内核权重。因此，在训练阶段计算原子特定的多核字典，SRC 使用该字典对测试图像进​​行分类。此外，证明了由此产生的优化问题是凸的，可以通过通用算法解决。