Sparse regression analysis for object recognition

Baochang Zhang; Shengping Zhang; Jianzhuang Liu

doi:10.1109/ICIP.2011.6116121

Sparse regression analysis for object recognition

Baochang Zhang^*
, Shengping Zhang
, Jianzhuang Liu

^*Corresponding author for this work

Beihang University
School of Computer Science and Technology, Harbin Institute of Technology
Chinese University of Hong Kong
Shenzhen Institute of Advanced Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper proposes a new method named Sparse Regression Analysis (SRA) for object representation and recognition. In SRA, ℓ ₁-norm minimization is combined with regression analysis to represent the input signal. The discriminative ability of SRA derives from the fact that the subset which most compactly expresses the input signal is activated in the regression analysis. To achieve a further improvement, Kernelized SRA (KSRA) is developed to make a nonlinear extension of SRA. The experiments are conducted on both palmprint and face recognition, which show that the proposed methods achieve a much better performance than sparse representation classifier, principal component analysis, and linear discriminant analysis.

Original language	English
Title of host publication	ICIP 2011
Subtitle of host publication	2011 18th IEEE International Conference on Image Processing
Pages	2381-2384
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2011.6116121
State	Published - 2011
Externally published	Yes
Event	2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium Duration: 11 Sep 2011 → 14 Sep 2011

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/Territory	Belgium
City	Brussels
Period	11/09/11 → 14/09/11

Keywords

Sparse representation
face recognition
palmprint recognition
ℓ norm minimization

Access to Document

10.1109/ICIP.2011.6116121

Cite this

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title = "Sparse regression analysis for object recognition",

abstract = "This paper proposes a new method named Sparse Regression Analysis (SRA) for object representation and recognition. In SRA, ℓ 1-norm minimization is combined with regression analysis to represent the input signal. The discriminative ability of SRA derives from the fact that the subset which most compactly expresses the input signal is activated in the regression analysis. To achieve a further improvement, Kernelized SRA (KSRA) is developed to make a nonlinear extension of SRA. The experiments are conducted on both palmprint and face recognition, which show that the proposed methods achieve a much better performance than sparse representation classifier, principal component analysis, and linear discriminant analysis.",

keywords = "Sparse representation, face recognition, palmprint recognition, ℓ norm minimization",

author = "Baochang Zhang and Shengping Zhang and Jianzhuang Liu",

year = "2011",

doi = "10.1109/ICIP.2011.6116121",

language = "英语",

isbn = "9781457713033",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "2381--2384",

booktitle = "ICIP 2011",

note = "2011 18th IEEE International Conference on Image Processing, ICIP 2011 ; Conference date: 11-09-2011 Through 14-09-2011",

}

Zhang, B, Zhang, S & Liu, J 2011, Sparse regression analysis for object recognition. in ICIP 2011: 2011 18th IEEE International Conference on Image Processing., 6116121, Proceedings - International Conference on Image Processing, ICIP, pp. 2381-2384, 2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, 11/09/11. https://doi.org/10.1109/ICIP.2011.6116121

Sparse regression analysis for object recognition. / Zhang, Baochang; Zhang, Shengping; Liu, Jianzhuang.
ICIP 2011: 2011 18th IEEE International Conference on Image Processing. 2011. p. 2381-2384 6116121 (Proceedings - International Conference on Image Processing, ICIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhang, Baochang

AU - Zhang, Shengping

AU - Liu, Jianzhuang

PY - 2011

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N2 - This paper proposes a new method named Sparse Regression Analysis (SRA) for object representation and recognition. In SRA, ℓ 1-norm minimization is combined with regression analysis to represent the input signal. The discriminative ability of SRA derives from the fact that the subset which most compactly expresses the input signal is activated in the regression analysis. To achieve a further improvement, Kernelized SRA (KSRA) is developed to make a nonlinear extension of SRA. The experiments are conducted on both palmprint and face recognition, which show that the proposed methods achieve a much better performance than sparse representation classifier, principal component analysis, and linear discriminant analysis.

AB - This paper proposes a new method named Sparse Regression Analysis (SRA) for object representation and recognition. In SRA, ℓ 1-norm minimization is combined with regression analysis to represent the input signal. The discriminative ability of SRA derives from the fact that the subset which most compactly expresses the input signal is activated in the regression analysis. To achieve a further improvement, Kernelized SRA (KSRA) is developed to make a nonlinear extension of SRA. The experiments are conducted on both palmprint and face recognition, which show that the proposed methods achieve a much better performance than sparse representation classifier, principal component analysis, and linear discriminant analysis.

KW - Sparse representation

KW - face recognition

KW - palmprint recognition

KW - ℓ norm minimization

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DO - 10.1109/ICIP.2011.6116121

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T2 - 2011 18th IEEE International Conference on Image Processing, ICIP 2011

Y2 - 11 September 2011 through 14 September 2011

ER -

Sparse regression analysis for object recognition

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Keywords

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