Abstract In this paper, we propose a dual subspace discriminative projection learning (DSDPL) framework for multi-category image classification. Our approach reflects the notion that images are composed of class-shared information, class-specific information, and sparse noise. Unlike traditional subspace learning methods, DSDPL serves to decompose original high dimensional data, via learned projection matrices, into class-shared and class-specific subspaces. The learned projection matrices are jointly constrained with l2,1 sparse norm and LDA terms while the reconstructive properties of DSDPL reduce information loss, leading to greater stability within low dimensional subspaces. This is combined with regression-based terms to facilitate a more robust classification approach, using more accurately extracted class-specific features for better classification. Our approach is examined on five different datasets for face, object and scene classifications. Experimental results demonstrate not only the superiority and versatility of DSDPL over current benchmark approaches, but also a more robust classification approach with low sample size training data.

中文翻译：

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双子空间判别投影学习

摘要 在本文中，我们提出了一种用于多类别图像分类的双子空间判别投影学习（DSDPL）框架。我们的方法反映了图像由类共享信息、特定于类的信息和稀疏噪声组成的概念。与传统的子空间学习方法不同，DSDPL 用于通过学习的投影矩阵将原始高维数据分解为类共享和特定于类的子空间。学习到的投影矩阵受到 l2,1 稀疏范数和 LDA 项的共同约束，而 DSDPL 的重构特性减少了信息丢失，从而在低维子空间内实现了更高的稳定性。这与基于回归的术语相结合，以促进更强大的分类方法，使用更准确提取的特定于类的特征来进行更好的分类。我们的方法在面部、物体和场景分类的五个不同数据集上进行了检查。实验结果不仅证明了 DSDPL 相对于当前基准方法的优越性和通用性，而且还证明了一种具有低样本量训练数据的更稳健的分类方法。