Weighted Sparse Image Classification Based on Low Rank Representation

• 作者: Qidi Wu Ruolin Zhou Yibing Li Yun Lin
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: IMAGE classification SPARSE REPRESENTATION low-rank REPRESENTATION NUMERICAL optimization.
• 年，卷（期）: 2018,（7）
• 页码范围: 91-105
• 页数: 15页
• 分类号: TP3

摘要

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 a supervised way.Experimental results show that the proposed method is superiority to many conventional image classification methods.