Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/200570
Title: Low-Rank Embedding for Robust Image Feature Extraction
Authors: Wai Keung Wong;Zhihui Lai;Jiajun Wen;Xiaozhao Fang;Yuwu Lu
Year: 2017
Publisher: IEEE
Abstract: Robustness to noises, outliers, and corruptions is an important issue in linear dimensionality reduction. Since the sample-specific corruptions and outliers exist, the class-special structure or the local geometric structure is destroyed, and thus, many existing methods, including the popular manifold learning- based linear dimensionality methods, fail to achieve good performance in recognition tasks. In this paper, we focus on the unsupervised robust linear dimensionality reduction on corrupted data by introducing the robust low-rank representation (LRR). Thus, a robust linear dimensionality reduction technique termed low-rank embedding (LRE) is proposed in this paper, which provides a robust image representation to uncover the potential relationship among the images to reduce the negative influence from the occlusion and corruption so as to enhance the algorithm's robustness in image feature extraction. LRE searches the optimal LRR and optimal subspace simultaneously. The model of LRE can be solved by alternatively iterating the argument Lagrangian multiplier method and the eigendecomposition. The theoretical analysis, including convergence analysis and computational complexity, of the algorithms is presented. Experiments on some well-known databases with different corruptions show that LRE is superior to the previous methods of feature extraction, and therefore, it indicates the robustness of the proposed method. The code of this paper can be downloaded from http://www.scholat.com/laizhihui.
URI: http://localhost/handle/Hannan/200570
volume: 26
issue: 6
More Information: 2905,
2917
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7893780.pdf2.49 MBAdobe PDF
Title: Low-Rank Embedding for Robust Image Feature Extraction
Authors: Wai Keung Wong;Zhihui Lai;Jiajun Wen;Xiaozhao Fang;Yuwu Lu
Year: 2017
Publisher: IEEE
Abstract: Robustness to noises, outliers, and corruptions is an important issue in linear dimensionality reduction. Since the sample-specific corruptions and outliers exist, the class-special structure or the local geometric structure is destroyed, and thus, many existing methods, including the popular manifold learning- based linear dimensionality methods, fail to achieve good performance in recognition tasks. In this paper, we focus on the unsupervised robust linear dimensionality reduction on corrupted data by introducing the robust low-rank representation (LRR). Thus, a robust linear dimensionality reduction technique termed low-rank embedding (LRE) is proposed in this paper, which provides a robust image representation to uncover the potential relationship among the images to reduce the negative influence from the occlusion and corruption so as to enhance the algorithm's robustness in image feature extraction. LRE searches the optimal LRR and optimal subspace simultaneously. The model of LRE can be solved by alternatively iterating the argument Lagrangian multiplier method and the eigendecomposition. The theoretical analysis, including convergence analysis and computational complexity, of the algorithms is presented. Experiments on some well-known databases with different corruptions show that LRE is superior to the previous methods of feature extraction, and therefore, it indicates the robustness of the proposed method. The code of this paper can be downloaded from http://www.scholat.com/laizhihui.
URI: http://localhost/handle/Hannan/200570
volume: 26
issue: 6
More Information: 2905,
2917
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7893780.pdf2.49 MBAdobe PDF
Title: Low-Rank Embedding for Robust Image Feature Extraction
Authors: Wai Keung Wong;Zhihui Lai;Jiajun Wen;Xiaozhao Fang;Yuwu Lu
Year: 2017
Publisher: IEEE
Abstract: Robustness to noises, outliers, and corruptions is an important issue in linear dimensionality reduction. Since the sample-specific corruptions and outliers exist, the class-special structure or the local geometric structure is destroyed, and thus, many existing methods, including the popular manifold learning- based linear dimensionality methods, fail to achieve good performance in recognition tasks. In this paper, we focus on the unsupervised robust linear dimensionality reduction on corrupted data by introducing the robust low-rank representation (LRR). Thus, a robust linear dimensionality reduction technique termed low-rank embedding (LRE) is proposed in this paper, which provides a robust image representation to uncover the potential relationship among the images to reduce the negative influence from the occlusion and corruption so as to enhance the algorithm's robustness in image feature extraction. LRE searches the optimal LRR and optimal subspace simultaneously. The model of LRE can be solved by alternatively iterating the argument Lagrangian multiplier method and the eigendecomposition. The theoretical analysis, including convergence analysis and computational complexity, of the algorithms is presented. Experiments on some well-known databases with different corruptions show that LRE is superior to the previous methods of feature extraction, and therefore, it indicates the robustness of the proposed method. The code of this paper can be downloaded from http://www.scholat.com/laizhihui.
URI: http://localhost/handle/Hannan/200570
volume: 26
issue: 6
More Information: 2905,
2917
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7893780.pdf2.49 MBAdobe PDF