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Discriminative Fisher Embedding Dictionary Learning Algorithm for Object Recognition

机译：识别性Fisher嵌入字典学习算法的目标识别

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摘要

Both interclass variances and intraclass similarities are crucial for improving the classification performance of discriminative dictionary learning (DDL) algorithms. However, existing DDL methods often ignore the combination between the interclass and intraclass properties of dictionary atoms and coding coefficients. To address this problem, in this paper, we propose a discriminative Fisher embedding dictionary learning (DFEDL) algorithm that simultaneously establishes Fisher embedding models on learned atoms and coefficients. Specifically, we first construct a discriminative Fisher atom embedding model by exploring the Fisher criterion of the atoms, which encourages the atoms of the same class to reconstruct the corresponding training samples as much as possible. At the same time, a discriminative Fisher coefficient embedding model is formulated by imposing the Fisher criterion on the profiles (row vectors of the coding coefficient matrix) and coding coefficients, which forces the coding coefficient matrix to become a block-diagonal matrix. Since the profiles can indicate which training samples are represented by the corresponding atoms, the proposed two discriminative Fisher embedding models can alternatively and interactively promote the discriminative capabilities of the learned dictionary and coding coefficients. The extensive experimental results demonstrate that the proposed DFEDL algorithm achieves superior performance in comparison with some state-of-the-art dictionary learning algorithms on both hand-crafted and deep learning-based features.
机译：类间差异和类内相似性对于提高判别词典学习（DDL）算法的分类性能都至关重要。但是，现有的DDL方法通常会忽略字典原子和编码系数的类间和类内属性之间的组合。为了解决这个问题，在本文中，我们提出了一种判别式Fisher嵌入字典学习（DFEDL）算法，该算法同时在学习的原子和系数上建立Fisher嵌入模型。具体来说，我们首先通过探索原子的Fisher准则来构造区分性Fisher原子嵌入模型，这鼓励相同类别的原子尽可能多地重建相应的训练样本。同时，通过在轮廓（编码系数矩阵的行向量）和编码系数上施加Fisher准则来建立区分式Fisher系数嵌入模型，这迫使编码系数矩阵变成块对角矩阵。由于配置文件可以指示相应的原子表示哪些训练样本，因此建议的两个判别式Fisher嵌入模型可以替代性地和交互式地促进学习字典和编码系数的判别能力。广泛的实验结果表明，与一些基于手工制作和基于深度学习的功能的最新词典学习算法相比，提出的DFEDL算法具有更高的性能。

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