Representation based classification methods have become a hot research topic during the past few years, and the two most prominent approaches are sparse representation based classification (SRC) and collaborative representation based classification (CRC). CRC reveals that it is the collaborative representation rather than the sparsity that makes SRC successful. Nevertheless, the dense representation of CRC may not be discriminative which will degrade its performance for classification tasks. To alleviate this problem to some extent, we propose a new method called sparse and collaborative-competitive representation based classification (SCCRC) for image classification. Firstly, the coefficients of the test sample are obtained by SRC and CCRC, respectively. Then the fused coefficient is derived by multiplying the coefficients of SRC and CCRC. Finally, the test sample is designated to the class that has the minimum residual. Experimental results on several benchmark databases demonstrate the efficacy of our proposed SCCRC. The source code of SCCRC is accessible at https://github.com/li-zi-qi/SCCRC.

中文翻译：

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稀疏和协作竞争表示的乘法融合用于图像分类

基于表示的分类方法已成为近年来的研究热点，而两种最突出的方法是基于稀疏表示的分类（SRC）和基于协作表示的分类（CRC）。CRC揭示，成功实现SRC的是协作代表而不是稀疏。但是，CRC的密集表示可能没有区别，这会降低其在分类任务中的性能。为了在某​​种程度上缓解这一问题，我们提出了一种新的方法，称为基于稀疏和基于协作竞争表示的分类（SCCRC）进行图像分类。首先，分别通过SRC和CCRC获得测试样本的系数。然后，通过将SRC和CCRC的系数相乘得出融合系数。最后，将测试样品指定为具有最小残留量的类别。在几个基准数据库上的实验结果证明了我们提出的SCCRC的功效。可从https://github.com/li-zi-qi/SCCRC访问SCCRC的源代码。