A novel Bayesian classifier with smaller eigenvalues reset by threshold based on given database

Guorong Xuan, Xiuming Zhu, Yun Q. Shi, Peiqi Chai, Xia Cui, Jue Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A novel Bayesian classifier with smaller eigenvalues reset by threshold based on database is proposed in this paper. The threshold is used to substitute eigenvalues of scatter matrices which are smaller than the threshold to minimize the classification error rate with a given database, thus improving the performance of Bayesian classifier. Several experiments have shown its effectiveness. The error rates of both handwritten number recognition with MNIST database and Bengali handwritten digit recognition are small by using the proposed method. The steganalyszing JPEG images using this proposed classifier performs well.

Original languageEnglish (US)
Title of host publicationImage Analysis and Recognition - 4th International Conference, ICIAR 2007, Proceedings
PublisherSpringer Verlag
Pages375-386
Number of pages12
ISBN (Print)9783540742586
DOIs
StatePublished - 2007
Event4th International Conference on Image Analysis and Recognition, ICIAR 2007 - Montreal, Canada
Duration: Aug 22 2007Aug 24 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4633 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Image Analysis and Recognition, ICIAR 2007
CountryCanada
CityMontreal
Period8/22/078/24/07

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Eigenvalues
  • Handwritten digit recognition
  • Improved Bayesian classifier
  • Steganalysis
  • Threshold

Fingerprint Dive into the research topics of 'A novel Bayesian classifier with smaller eigenvalues reset by threshold based on given database'. Together they form a unique fingerprint.

Cite this