A fast procedure for the computation of similarities between gaussian HMMS

Ling Chen, Hong Man

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

Abstract

An appropriate definition and efficient computation of similarity (or distance) measures between stochastic models are of theoretical and practical interest. In this work a similarity measure for Gaussian hidden Markov models is introduced based on the generalized probability product kernel. An efficient scheme for computing the similarity measure is presented. The out of precision problem, which is a significant implementation issue, is considered and a scaling procedure is provided. The effectiveness of the proposed method has been evaluated on texture classification and preliminary experimental results are presented.

Original languageEnglish
Title of host publication2004 International Conference on Image Processing, ICIP 2004
Pages1513-1516
Number of pages4
DOIs
StatePublished - 2004
Event2004 International Conference on Image Processing, ICIP 2004 - , Singapore
Duration: 18 Oct 200421 Oct 2004

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume3
ISSN (Print)1522-4880

Conference

Conference2004 International Conference on Image Processing, ICIP 2004
Country/TerritorySingapore
Period18/10/0421/10/04

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