论文标题
稀疏和协作竞争性表示图像分类的乘法融合
Multiplication fusion of sparse and collaborative-competitive representation for image classification
论文作者
论文摘要
在过去的几年中，基于表示形式的分类方法已成为一个热门研究主题，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透露，使SRC成功的是协作代表，而不是稀疏性。然而，CRC的密集表示可能不会具有歧视性，这会降低其在分类任务中的绩效。为了在某种程度上减轻这个问题，我们提出了一种新方法，称为基于图像分类的基于稀疏和协作竞争性表示的分类（SCCRC）。首先，测试样品的系数分别通过SRC和CCRC获得。然后，通过乘以SRC和CCRC的系数来得出融合系数。最后，将测试样本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的功效。 SCCRC的源代码可在https://github.com/li-zi-qi/sccrc上访问。
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.
