Bi-dierectional PCA with assembled matrix distance metric

Wangmeng Zuo; Kuanquan Wang; Dapeng Zhang

doi:10.1109/ICIP.2005.1530216

Bi-dierectional PCA with assembled matrix distance metric

Wangmeng Zuo, Kuanquan Wang, Dapeng Zhang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

23 Citations (Scopus)

Abstract

Principal Component Analysis (PCA) has been very successful in image recognition. Recent researches on PCAbased methods are mainly concentrated on two issues, feature extraction and classification. In this paper we propose BiDirectional PCA (BDPCA) with assembled matrix distance (AMD) metric to simultaneously deal with these two issues. For feature extraction, we propose a BDPCA approach which can reduce the dimension of the original image matrix in both column and row directions. For classification, we present an AMD metric to calculate the distance between two feature matrices. The results of our experiments show that, BDPCA with AMD metric is very effective in image recognition.

Original language	English
Title of host publication	IEEE International Conference on Image Processing 2005, ICIP 2005
Pages	958-961
Number of pages	4
Volume	2
DOIs	https://doi.org/10.1109/ICIP.2005.1530216
Publication status	Published - 1 Dec 2005
Event	IEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy Duration: 11 Sept 2005 → 14 Sept 2005

Conference

Conference	IEEE International Conference on Image Processing 2005, ICIP 2005
Country/Territory	Italy
City	Genova
Period	11/09/05 → 14/09/05

Keywords

2DPCA
Face recognition
Image recognition
Palmprint recognition
PCA

ASJC Scopus subject areas

General Engineering

More information

10.1109/ICIP.2005.1530216

Cite this

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title = "Bi-dierectional PCA with assembled matrix distance metric",

abstract = "Principal Component Analysis (PCA) has been very successful in image recognition. Recent researches on PCAbased methods are mainly concentrated on two issues, feature extraction and classification. In this paper we propose BiDirectional PCA (BDPCA) with assembled matrix distance (AMD) metric to simultaneously deal with these two issues. For feature extraction, we propose a BDPCA approach which can reduce the dimension of the original image matrix in both column and row directions. For classification, we present an AMD metric to calculate the distance between two feature matrices. The results of our experiments show that, BDPCA with AMD metric is very effective in image recognition.",

keywords = "2DPCA, Face recognition, Image recognition, Palmprint recognition, PCA",

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AU - Zuo, Wangmeng

AU - Wang, Kuanquan

AU - Zhang, Dapeng

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N2 - Principal Component Analysis (PCA) has been very successful in image recognition. Recent researches on PCAbased methods are mainly concentrated on two issues, feature extraction and classification. In this paper we propose BiDirectional PCA (BDPCA) with assembled matrix distance (AMD) metric to simultaneously deal with these two issues. For feature extraction, we propose a BDPCA approach which can reduce the dimension of the original image matrix in both column and row directions. For classification, we present an AMD metric to calculate the distance between two feature matrices. The results of our experiments show that, BDPCA with AMD metric is very effective in image recognition.

AB - Principal Component Analysis (PCA) has been very successful in image recognition. Recent researches on PCAbased methods are mainly concentrated on two issues, feature extraction and classification. In this paper we propose BiDirectional PCA (BDPCA) with assembled matrix distance (AMD) metric to simultaneously deal with these two issues. For feature extraction, we propose a BDPCA approach which can reduce the dimension of the original image matrix in both column and row directions. For classification, we present an AMD metric to calculate the distance between two feature matrices. The results of our experiments show that, BDPCA with AMD metric is very effective in image recognition.

KW - 2DPCA

KW - Face recognition

KW - Image recognition

KW - Palmprint recognition

KW - PCA

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U2 - 10.1109/ICIP.2005.1530216

DO - 10.1109/ICIP.2005.1530216

M3 - Conference article published in proceeding or book

SN - 0780391349

SN - 9780780391345

VL - 2

SP - 958

EP - 961

BT - IEEE International Conference on Image Processing 2005, ICIP 2005

T2 - IEEE International Conference on Image Processing 2005, ICIP 2005

Y2 - 11 September 2005 through 14 September 2005

ER -

Bi-dierectional PCA with assembled matrix distance metric

Abstract

Conference

Keywords

ASJC Scopus subject areas

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