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Sparse representation for image recognition based on semi-genetic algorithm in feature space
- Chinese Journal of Intelligent Science and Technology Vol. 3, Issue 3, Pages: 359-369(2021)
- 作者机构：
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202137
CLC： TP391.4-
Published Online：2021-09，
Published：15 September 2021
- 稿件说明：
移动端阅览
Linrui SHI, Yijing HUANG, Jinwu FU, et al. Sparse representation for image recognition based on semi-genetic algorithm in feature space[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 359-369.
Linrui SHI, Yijing HUANG, Jinwu FU, et al. Sparse representation for image recognition based on semi-genetic algorithm in feature space[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 359-369. DOI： 10.11959/j.issn.2096-6652.202137.
摘要
经典的稀疏表示分类（SRC）通常是基于求解L
1
最小化问题的。SRC在原始输入空间中求解L
0
范数最小化问题，无法很好地获取数据中的非线性信息。为了解决这一问题，应用非线性映射将原始输入数据映射到一个新的高维特征空间，并提出了一种新的基于L
0
范数的表示方法。在所提方法中，表示测试样本的字典包含两个部分：第一部分固定在测试样本的近邻；第二部分的训练样本通过半遗传算法（SGA）来选择，利用表示误差确定第二部分的表示字典。在所提方法中，如果训练样本和已确定的测试样本的近邻产生最小表示误差，那么这些训练样本将被 SGA 确定为表示字典的第二部分。在一些常用的人脸数据集和一个手写体数据集上的实验表明，所提方法能够获得更好的分类性能。
Abstract
The typical sparse representation for classification (SRC) is usually based on L
1
minimization problem.Conceptually
SRC is essentially an L
0
norm minimization problem solved in the original input space
which cannot capture well the nonlinear information within the data.In order to address this problem
a n
onlinear mapping to map the original input data into a new high dimensional feature space was applied
and a new representation approach based on L
0
norm was proposed.The representing dictionary used to represent the test sample contains two parts in the proposed approach.The first part is fixed to the neighbors of the test sample.The training samples of the second part is chosen by the variation of genetic algorithm (GA)
i.e.
the semi GA (SGA) algorithm
which exploits the representation error to determine the second part of the representing dictionary.In the approach
if the training samples combining the determined neighbors of the test sample yield the least representation error
these training samples are determined as the second part of the representing dictionary by SGA.Experiments on several popular face databases and one handwritten digit data set demonstrate that the proposed approach can achieve better classification performance.
关键词
Keywords
references
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