4.7 Article

Class mean-weighted discriminative collaborative representation for classification

期刊

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
卷 36, 期 7, 页码 3144-3173

出版社

WILEY
DOI: 10.1002/int.22411

关键词

collaborative representation; pattern recognition; representation‐ based classification

资金

  1. National Natural Science Foundation of China [61976107, 61672268, 61962010, 61502208]
  2. International Postdoctoral Exchange Fellowship Program of China Postdoctoral Council [20180051]
  3. Research Foundation for Talented Scholars of JiangSu University [14JDG037]

向作者/读者索取更多资源

This research introduces a novel method based on collaborative representation classification for image classification tasks, demonstrating superior performance and effective optimization. By incorporating innovative class mean-weighted and decorrelating regularization terms, it effectively enhances classification performance.
Representation-based classification (RBC) has been attracting a great deal of attention in pattern recognition. As a typical extension to RBC, collaborative representation-based classification (CRC) has demonstrated its superior performance in various image classification tasks. Ideally, we expect that the learned class-specific representations for a testing sample are discriminative, and the representation computed for the true class dominates the final representation of the testing sample. Most existing CRC-based methods can learn pattern discrimination, but cannot differentiate the contribution of class-specific representations to the classification of each testing sample. It is challenging for a representation-based classifier to retain both properties. To address this challenge and further improve CRC's classification performance, we propose a novel CRC-based method, class mean-weighted discriminative collaborative representation-based classifier (CMW-DCRC). Its objective function penalises the standard l 2-norm residuals with two discriminative regularisation terms. A decorrelating term makes the class-specific representations more discriminative, and a newly designed class mean-weighted term that promotes the training samples from individual classes to competitively reconstruct the testing sample while boosting the contribution of the true class. To further enhance the robustness of CRC, we extend CMW-DCRC by replacing the l(2)-norm coding residual with a l(1)-norm coding residual, and solve the optimisation problem with an iteratively reweighted least square algorithm. Extensive experimental results on nine image data sets have shown that our methods outperform the state-of-the-art RBC-based methods.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据