Variational inference for conditional random fields

Chih Pin Liao*, Jen-Tzung Chien

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Conditional random fields (CRFs) have been popular for contextual pattern classification. This paper presents two variational inference methods for direct approximation of a conditional probability instead of indirect calculation through Viterbi approximation of a marginal probability. The CRFs with the factorized variational inference (FVI) and the structured variational inference (SVI) are proposed and investigated for human motion recognition. In general, FVI assumes a factorization of variational distributions of individual states for representation of conditional probability while SVI preserves the state structure in the variational distribution. In the experiments on using IDIAP human motion database, we found that CRFs using variation inference methods performed better than baseline CRFs using Viterbi approximation. CRFs with SVI obtained higher classification accuracy than those with FVI.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages2002-2005
Number of pages4
DOIs
StatePublished - 8 Nov 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: 14 Mar 201019 Mar 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period14/03/1019/03/10

Keywords

  • Learning systems
  • Pattern recognition
  • Variational methods
  • Video signal processing

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