Discriminative structured dictionary learning for image classification

被引：2
作者：
Wang P. [1 ]
Lan J. [1 ]
Zang Y. [2 ]
Song Z. [1 ]
机构：
[1] School of Sciences, Tianjin University, Tianjin
[2] Tianjin Tendbeyond Science and Technology Development Co., Ltd, Tianjin
来源：
Transactions of Tianjin University | 2016年 / 22卷 / 2期
关键词：
dictionary learning; image classification; sparse coding; sparse representation;
D O I：
10.1007/s12209-016-2624-z
中图分类号：
学科分类号：
摘要：
In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary’s discriminative power, the reconstruction error, classification error and inhomogeneous representation error are integrated into the objective function. The proposed approach learns a single structured dictionary and a linear classifier jointly. The learned dictionary encourages the samples from the same class to have similar sparse codes, and the samples from different classes to have dissimilar sparse codes. The solution to the objective function is achieved by employing a feature-sign search algorithm and Lagrange dual method. Experimental results on three public databases demonstrate that the proposed approach outperforms several recently proposed dictionary learning techniques for classification. © 2016, Tianjin University and Springer-Verlag Berlin Heidelberg.
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收藏
页码：158 / 163
页数：5
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