基于压缩感知原理的融合判别信息的协作表示方法

提出了一种用于视觉分类任务的低计算复杂度且有效的图像表示方法。把协作表示和判别信息结合在统一框架内，是基于协作表示分类方法的一种扩展形式。测试样本的协作表示系数是稀疏的，这种基于冗余和过完备的表示对于遮挡和伪装而言是鲁棒的；此外，通过最小化类内散布矩阵和最大化类间散布矩阵的判别信息的挖掘，对于视觉分类问题也是很有帮助的。在一些基准数据库上的实验表明，提出的方法相对于现有的方法而言能够获得更有竞争力的表现。

Discriminative and Collaborative Representation for visual classification based on Compressive sensing

In this paper, we proposed a low computation complexity, yet very efficient representation of image for visual classification tasks. We combine the collaborative representation with discriminative ingredient together in a unified framework, which is an extended version of collaborative representation based classification. The coefficients of collaborative representation of test samples are sparsely and robust to occlusion or other disguises based on redundant and over-complete dictionary. Besides, the discriminative information is exploited by minimizing the within-class scatter and maximizing the between-class scatter, which is very helpful for visual classification tasks. Experimental results on some widely used benchmark datasets indicate that the proposed method could achieve competitive performance with other existing works.