In analysis dictionary learning (ADL) algorithms, the row vectors (profiles) of the analysis coefficient matrix and analysis atoms are always one-to-one correspondence, and the analysis information of atoms could be represented by their corresponding profiles. However, the analysis atoms and their corresponding profiles are seldom jointly explored to formulate a discrimination term. In this paper, we exploit the analysis atoms and profiles to design a structured discriminative ADL algorithm for image classification, called structured analysis dictionary learning based on discriminative Fisher pair (SADL-DFP). Specifically, we explicitly provide the definitions of the profile and the newly defined profile block, which are used to illustrate the analysis mechanism of the ADL model. Then, the discriminative Fisher pair (DFP) model is designed by using the Fisher criterion of analysis atoms and profiles, which can enhance the inter-class separability and intra-class compactness of the analysis atoms and profiles. Since the profiles and analysis atoms can be updated alternatively and interactively, our DFP model can further encourage the analysis atoms to analyze the same-class training samples as much as possible. In addition, a robust multiclass classifier is simultaneously learned by utilizing the label information of the training samples and analysis atoms in our SADL-DFP algorithm. The experimental results show that the proposed SADL-DFP algorithm can outperform many state-of-the-art dictionary learning algorithms on multiple datasets with both deep learning-based features and hand-crafted features.

在分析字典学习（ADL）算法中，分析系数矩阵和分析原子的行向量（轮廓）始终是一一对应的，原子的分析信息可以用它们对应的轮廓表示。但是，很少共同探索分析原子及其对应的轮廓来制定区分项。在本文中，我们利用分析原子和轮廓来设计用于图像分类的结构化判别ADL算法，称为基于判别Fisher对的结构化分析字典学习（SADL-DFP）。具体来说，我们显式提供概要文件和新定义的概要文件块的定义，这些定义用于说明ADL模型的分析机制。然后，区分费舍尔对（DFP）模型是通过使用分析原子和分布图的Fisher准则设计的，可以增强分析原子和分布图的类间可分离性和类内紧实性。由于配置文件和分析原子可以交替交互地更新，因此我们的DFP广告管理系统模型可以进一步鼓励分析原子尽可能多地分析同一类别的训练样本。此外，通过在我们的SADL-DFP算法中利用训练样本和分析原子的标签信息，可以同时学习一个健壮的多类分类器。实验结果表明，所提出的SADL-DFP算法在基于深度学习的特征和手工特征的多个数据集上，性能优于许多最新的字典学习算法。