北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2022, Vol. 45 ›› Issue (5): 85-90,128.

• 论文 • 上一篇    下一篇

基于正交投影学习的图像特征提取算法

张小乾1,谈振1,王1,梁芹1,万黎明2   

  1. 1.西南科技大学 信息工程学院  2.西南科技大学 计算机科学与技术学院


  • 收稿日期:2021-10-05 修回日期:2022-01-06 出版日期:2022-10-28 发布日期:2022-11-01
  • 通讯作者: 张小乾 E-mail:zhxq0528@163.com
  • 基金资助:
    四川省科技计划项目

Image Feature Extraction Algorithm Based on Orthogonal Projection Learning

ZHANG Xiaoqian1, TAN Zhen1, WANG Xiao1, LIANG Qin1, WAN Liming2 #br#   

  • Received:2021-10-05 Revised:2022-01-06 Online:2022-10-28 Published:2022-11-01

摘要: 为了改进低秩嵌入在数据重构和噪声抑制方面存在的不足,提高特征的识别准确度,提出了一种基于正交投影学习的图像特征提取算法,设计了半二次方的交替方向乘子法用于求解正交投影学习模型该模型通过引入正交矩阵保留样本的主要特征,引入范数约束使提取的特征更加显著;使用加权 Schatten p 范数来逼近秩的最优解为提高模型的鲁棒性并使其适用于有监督场景,将广义相关熵用于数据项建模和分类损失函数的构建在不同规模数据集上的实验结果表明,所提模型具有比现有其他模型更优良的特征提取性能

关键词: 图像特征提取, 加权 Schatten p 范数, 低秩表示, 投影学习

Abstract:

To overcome the deficiencies of low-rank embedding in data reconstruction and noise suppression, and improve the accuracy of its feature recognition,an image feature extraction algorithm is proposed based on orthogonal projection learning. The half-quadratic alternating direction method of multipliers algorithm is designed to solve the orthogonal projection learning model. The model retains the main features of the samples by introducing an orthogonal matrix,the norm constraints makes the extracted features more prominent, and the weighted Schatten p-Norm is used to approximate the optimal solution of the rank. To improve the robustness of the model and make it suitable for supervised scenarios, generalized correntropy is used for data item modeling and classification loss function construction. Experimental results on different scale datasets show that the proposed model has better feature extraction performance than other existing models.

Key words: image feature extraction, weighted Schatten p-norm, lowrank representation, projection learning

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