Bi-directional PCA with assembled matrix distance metric

W. Zuo, K. Wang, Dapeng Zhang

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

Abstract

Principal Component Analysis (PCA) has been very successful in image recognition. Recent researches on PCA-based methods are mainly concentrated on two issues, feature extraction and classification. In this paper we propose Bi-Directional 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 publication2005 ICIP : 2005 International Conference on Image Processing (ICIP) : September 11-14, 2005, Genova, Italy
PublisherIEEE
ISBN (Print)0780391349
Publication statusPublished - 2005
EventIEEE International Conference on Image Processing [ICIP] -
Duration: 1 Jan 2005 → …

Conference

ConferenceIEEE International Conference on Image Processing [ICIP]
Period1/01/05 → …

Keywords

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

Cite this