Two-Dimensional Soft Linear Discriminant Projection for Robust Image Feature Extraction and Recognition

2016 ◽  
pp. 514-521
Author(s):  
Yu Tang ◽  
Zhao Zhang ◽  
Weiming Jiang
Keyword(s):  
Feature Extraction ◽  
Image Feature ◽  
Two Dimensional ◽  
Image Feature Extraction ◽  
Linear Discriminant ◽  
Robust Image ◽  
Discriminant Projection

Sparse fuzzy two-dimensional discriminant local preserving projection (SF2DDLPP) for robust image feature extraction

2021 ◽  
Vol 563 ◽  
pp. 1-15 ◽  
Author(s):  
Minghua Wan ◽  
Xueyu Chen ◽  
Tianming Zhan ◽  
Chao Xu ◽  
Guowei Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Feature ◽  
Two Dimensional ◽  
Image Feature Extraction ◽  
Robust Image

Orthogonal Low-rank Projection Learning for Robust Image Feature Extraction

2021 ◽  
pp. 1-1
Author(s):  
Xiaoqian Zhang ◽  
Zhen Tan ◽  
Huaijiang Sun ◽  
Zungang Wang ◽  
Mingwei Qin
Keyword(s):  
Feature Extraction ◽  
Image Feature ◽  
Low Rank ◽  
Image Feature Extraction ◽  
Robust Image

A New Extension of Locality Preserving Projections for Image Feature Extraction

2012 ◽  
Vol 241-244 ◽  
pp. 1715-1718
Author(s):  
Guo Hong Huang
Keyword(s):  
Feature Extraction ◽  
Local Information ◽  
Image Feature ◽  
Two Dimensional ◽  
Two Stage ◽  
Image Feature Extraction ◽  
Locality Preserving ◽  
Image Manifold ◽  
New Framework ◽  
Novel Algorithm

This paper proposes a novel algorithm for image feature extraction, namely, the two-directional two-dimensional locality preserving projection, ((2D)2LPP), which can find an embedding from two directions that not only preserves local information and detect the intrinsic image manifold structure, but also combines the both information between rows and those between columns simultaneously. We also combine the advantages of (2D)2LPP and LDA, and propose a new framework for feature extraction as two-stage: “(2D)2LPP+LDA.” The LDA step is performed to further reduce the dimension of feature matrix in the (2D)2LPP subspace. Experimental results on ORL face databases demonstrate the effectiveness of the proposed methods.

Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition

2010 ◽  
Vol 121-122 ◽  
pp. 391-398 ◽  
Author(s):  
Qi Rong Zhang ◽  
Zhong Shi He
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Image Feature ◽  
Two Dimensional ◽  
Face Database ◽  
Image Feature Extraction ◽  
Locality Preserving Projection ◽  
Locality Preserving

In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional locality discriminant preserving projections (2DLDPP). Two-dimensional locality preserving projections (2DLPP) can direct on 2D image matrixes. So, it can make better recognition rate than locality preserving projection. We investigate its more. The 2DLDPP is to use modified maximizing margin criterion (MMMC) in 2DLPP and set the parameter optimized to maximize the between-class distance while minimize the within-class distance. Extensive experiments are performed on ORL face database and FERET face database. The 2DLDPP method achieves better face recognition performance than PCA, 2DPCA, LPP and 2DLPP.

Low-Rank Embedding for Robust Image Feature Extraction

2017 ◽  
Vol 26 (6) ◽  
pp. 2905-2917 ◽  
Author(s):  
Wai Keung Wong ◽  
Zhihui Lai ◽  
Jiajun Wen ◽  
Xiaozhao Fang ◽  
Yuwu Lu
Keyword(s):  
Feature Extraction ◽  
Image Feature ◽  
Low Rank ◽  
Image Feature Extraction ◽  
Robust Image
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