A novel supervised dimensionality reduction algorithm for online image recognition

Fengxi Song, Dapeng Zhang, Qinglong Chen, Jingyu Yang

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

1 Citation (Scopus)

Abstract

Image recognition on streaming data is one of the most challenging topics in Image and Video Technology and incremental dimensionality reduction algorithms play a key role in online image recognition. In this paper, we present a novel supervised dimensionality reduction algorithm-Incremental Weighted Karhunen-Loève expansion based on the Between-class scatter matrix (IWKLB) for image recognition on streaming data. In comparison with Incremental PCA, IWKLB is more effective in terms of recognition rate. In comparison with Incremental LDA, it is free of small sample size problems and can directly be applied to high-dimensional image spaces with high efficiency. Experimental results conducted on AR, one benchmark face image database, demonstrate that IWKLB is more effective than IPCA and ILDA.
Original languageEnglish
Title of host publicationAdvances in Image and Video Technology - First Pacific Rim Symposium, PSIVT 2006, Proceedings
Pages198-207
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2006
Event1st Pacific Rim Symposium on Image and Video Technology, PSIVT 2006 - Hsinchu, Taiwan
Duration: 10 Dec 200613 Dec 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4319 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st Pacific Rim Symposium on Image and Video Technology, PSIVT 2006
Country/TerritoryTaiwan
CityHsinchu
Period10/12/0613/12/06

Keywords

  • Dimensionality reduction
  • Image recognition
  • Incremental algorithm
  • Streaming data
  • Supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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