[1]李静,陈秀宏.基于竞争性协同表示的局部判别投影特征提取[J].智能系统学报,2019,14(05):974-981.[doi:10.11992/tis.201809020]
 LI Jing,CHEN Xiuhong.Competitive collaborative representation-based local discriminant projection for feature extraction[J].CAAI Transactions on Intelligent Systems,2019,14(05):974-981.[doi:10.11992/tis.201809020]
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基于竞争性协同表示的局部判别投影特征提取(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年05期
页码:
974-981
栏目:
出版日期:
2019-09-05

文章信息/Info

Title:
Competitive collaborative representation-based local discriminant projection for feature extraction
作者:
李静 陈秀宏
江南大学 数字媒体学院, 江苏 无锡 214000
Author(s):
LI Jing CHEN Xiuhong
School of Digital Media, Jiangnan University, Wuxi 214000, China
关键词:
特征提取协同表示模式识别正系数竞争性鲁棒性局部结构人脸图像
Keywords:
feature extractioncollaborative representationpattern recognitionpositive coefficientcompetitiverobustnesslocal structureface image
分类号:
TP391.4
DOI:
10.11992/tis.201809020
摘要:
特征提取算法中利用样本间的协同表示关系构造邻接图只考虑所有训练样本的协同能力,而忽视了每一类训练样本的内在竞争能力。为此,本文提出一种基于竞争性协同表示的局部判别投影特征提取算法(competitive collaborative repesentation-based local discrininant projection for feature extraction,CCRLDP),该算法利用基于具有竞争性协同表示的方法构造类间图和类内图,考虑到邻接图中各类型系数的影响,引入保留正表示系数的思想稀疏化邻接图,通过计算类内散度矩阵和类间散度矩阵来刻画图像的局部结构并得其最优投影矩阵。在一些数据集上的实验结果表明,相比同类基于局部判别投影的特征提取算法,该算法具有很高的识别率,并在噪声和遮挡上具有良好的鲁棒性,该算法能有效地提高图像的识别效率。
Abstract:
The feature extraction algorithm uses the cooperative representation relation between samples to construct the adjacency graph, which only considers the synergy of all training samples and ignores the competitiveness of each type of training sample. Therefore, based on competitive cooperative representation, this study proposes a local discriminant projection feature extraction algorithm and further constructs between-class and within-class graphs. Considering the influence of each type of coefficient in the adjacency graph, we introduce the idea of retaining the positive representation coefficient in the sparse adjacency graph. The local structure of the image is characterized by calculating the within-class and between-class scatter matrices; furthermore, the optimal projection matrix is obtained. The experimental results of some data sets show that compared with similar feature extraction algorithms based on local discriminant projection, the algorithm exhibits good recognition effect and good robustness in noise and occlusion and effectively increases the image recognition efficiency.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-09-13。
基金项目:江苏省研究生科研创新计划项目(KYCX17_1500).
作者简介:李静,女,1994年生,硕士研究生,主要研究方向为数字图像处理、模式识别;陈秀宏,男,1964年生,教授,主要研究方向为数字图像处理和模式识别、目标检测与跟踪、优化理论与方法。发表学术论文120余篇。
通讯作者:李静.E-mail:944651524@qq.com
更新日期/Last Update: 1900-01-01