What is it about?

We present an effective SA-WCRC approach, which jointly takes sparsity of the coefficient and locality information of samples into account. Experiments on three face databases and one scene dataset demonstrate the superiority of SA-WCRC over its counterparts.

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Why is it important?

Both the augmented coefficient and classification scheme make SA-WCRC efficient for classification.


By augmenting the dense representation with a sparse representation, the wrong classification can be avoided. This demonstrates the efficacy of our proposed SA-WCRC.

Dr. Zi-Qi Li
Jiangnan University

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This page is a summary of: Sparsity augmented weighted collaborative representation for image classification, Journal of Electronic Imaging, October 2019, SPIE,
DOI: 10.1117/1.jei.28.5.053032.
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