Multiplication fusion of sparse and collaborative-competitive representation for image classification
This work addresses performance issues in image classification methods, but it is incremental as it builds on existing SRC and CRC approaches.
The paper tackled the problem of dense representation in collaborative representation based classification (CRC) degrading performance for image classification by proposing a new method called sparse and collaborative-competitive representation based classification (SCCRC), which fuses coefficients from SRC and CCRC via multiplication and achieves efficacy as demonstrated on several benchmark databases.
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.