Sparse fuzzy two-dimensional discriminant local preserving projection (SF2DDLPP) for robust image feature extraction
Sparse fuzzy two-dimensional discriminant local preserving projection (SF2DDLPP) for robust image feature extraction
复制标题
用于鲁棒图像特征提取的稀疏模糊二维判别局部保留投影（SF2DDLPP）
DOI：
10.1016/j.ins.2021.02.006
复制
发表时间：
2021-03-04
影响因子：
8.1
通讯作者：
Zhou, Huiting
中科院分区：
文献类型：
--
作者：
Wan, Minghua;Chen, Xueyu;Zhou, Huiting
近年来，基于二维判别局部保持投影（2DDLPP）算法的图像特征提取算法在许多领域得到了成功的应用。2DDLPP可以使用二维图像表示数据来保持局部固有流形结构的辨别信息。然而，2DDLPP算法遇到的重叠点（离群值）的敏感性的问题，并需要在现实世界中的应用高的计算成本。为了解决上述问题，我们引入了一种新的弹性特征提取算法，称为稀疏模糊2D判别局部保持投影（SF 2DDLPP）。首先，使用模糊k-近邻（FKNN）计算隶属度矩阵，并将其应用于类内加权矩阵和类间加权矩阵。其次，给出了直接求解广义本征函数的两个定理。最后，利用弹性网络回归方法回归最优稀疏模糊二维鉴别投影矩阵。在ORL、Yale、AR和Yale B人脸、USPS和掌纹数据集上的实验表明了该算法的有效性和稳定性。(c)2021爱思唯尔公司All rights reserved.
Recently, image feature extraction algorithms based on 2D discriminant local preserving projection (2DDLPP) algorithms have been successfully applied in many fields. The 2DDLPP can maintain the discrimination information of the local intrinsic manifold structure using two-dimensional image representation data. However, the 2DDLPP algorithm encounters the problem of the sensitivity of overlapping points (outliers) and requires high computational cost in real-world applications. In order to resolve the problems mentioned above, we introduce a new elastic feature extraction algorithm called the sparse fuzzy 2D discriminant local preserving projection (SF2DDLPP). First, the membership matrix is calculated using the fuzzy k-nearest neighbours (FKNN), which is applied to the intraclass weighted matrix and the interclass weighted matrix. Second, two theorems are developed to directly solve the generalized eigenfunctions. Finally, the optimal sparse fuzzy 2D discriminant projection matrix is regressed using the elastic net regression. The experiments show the effectiveness and stability of this algorithm on several face (ORL, Yale, AR and Yale B), USPS and palm print datasets.(c) 2021 Elsevier Inc. All rights reserved.