In recent years, deep dictionary learning (DDL)has attracted a great amount of attention due to its effectiveness for representation learning and visual recognition.~However, most existing methods focus on unsupervised deep dictionary learning, failing to further explore the category information.~To make full use of the category information of different samples, we propose a novel deep dictionary learning model with an intra-class constraint (DDLIC) for visual classification. Specifically, we design the intra-class compactness constraint on the intermediate representation at different levels to encourage the intra-class representations to be closer to each other, and eventually the learned representation becomes more discriminative.~Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage. Experimental results on four image datasets show that our method is superior to the state-of-the-art methods.

翻译：近年来,深字典学习(DDL)因其在代表性学习和视觉认知方面的效力而吸引了大量关注。 ~ 然而,大多数现有方法侧重于未经监督的深字典学习,未能进一步探索类别信息。 ~ 为了充分利用不同样本的类别信息,我们提议了一个新的深字典学习模式,在视觉分类方面带有阶级内部限制(DLIC)。 具体地说,我们设计了对不同级别的中间代表的阶级内部紧凑性限制,以鼓励阶级内部代表更加接近,最终,学习到的代表性变得更具有歧视性。 ~ 不像传统的DDL方法,在分类阶段,我们的DLIC以与培训阶段相似的方式进行层层次的贪婪优化。 四个图像数据集的实验结果显示,我们的方法优于最先进的方法。