Bi-dierectional PCA with assembled matrix distance metric

Wangmeng Zuo, Kuanquan Wang, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

22 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 languageEnglish
Title of host publicationIEEE International Conference on Image Processing 2005, ICIP 2005
Pages958-961
Number of pages4
Volume2
DOIs
Publication statusPublished - 1 Dec 2005
EventIEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy
Duration: 11 Sep 200514 Sep 2005

Conference

ConferenceIEEE International Conference on Image Processing 2005, ICIP 2005
CountryItaly
CityGenova
Period11/09/0514/09/05

Keywords

  • 2DPCA
  • Face recognition
  • Image recognition
  • Palmprint recognition
  • PCA

ASJC Scopus subject areas

  • Engineering(all)

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