Qidi Wu1, Yibing Li1, Yun Lin1,*, Ruolin Zhou2
CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 91-105, 2018, DOI: 10.3970/cmc.2018.02771
Abstract The conventional sparse representation-based image classification usually codes the samples independently, which will ignore the correlation information existed in the data. Hence, if we can explore the correlation information hidden in the data, the classification result will be improved significantly. To this end, in this paper, a novel weighted supervised spare coding method is proposed to address the image classification problem. The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation. And then, it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in… More >
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