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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用于图像分类的稀疏和协同竞争表示的乘法融合

基于表示的分类方法在过去几年成为一个热门的研究课题，其中最突出的两种方法是基于稀疏表示的分类（SRC）和基于协同表示的分类（CRC）。CRC 表明，使 SRC 成功的是协作表示而不是稀疏性。然而，CRC 的密集表示可能没有辨别力，这会降低其分类任务的性能。为了在一定程度上缓解这个问题，我们提出了一种新的图像分类方法，称为基于稀疏和协同竞争表示的分类（SCCRC）。首先，分别通过SRC和CCRC得到测试样本的系数。然后通过将SRC和CCRC的系数相乘得到融合系数。最后，测试样本被指定为具有最小残差的类。几个基准数据库的实验结果证明了我们提出的 SCCRC 的有效性。SCCRC 的源代码可在 https://github.com/li-zi-qi/SCCRC 获得。