This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose cross-label suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the cross-label suppression, we don't resort to frequently-used $\ell_0$-norm or $\ell_1$-norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets including face recognition, object categorization, scene classification, texture recognition and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency.

该论文提出了一种通过学习紧凑且具有区分性的字典来实现图像分类的方法，其中引入了交叉标签抑制和分组正则化等约束策略来提高分类性能，并基于该字典实现了两种简单的分类方案，并在多个数据集上进行了实验证明了该方法具有更高的识别准确率和计算效率。

跨标签压制：具有群组正则化的鉴别性快速字典学习