TY - GEN
T1 - A fast procedure for the computation of similarities between gaussian HMMS
AU - Chen, Ling
AU - Man, Hong
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=20444499002&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=20444499002&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2004.1421352
DO - 10.1109/ICIP.2004.1421352
M3 - Conference contribution
AN - SCOPUS:20444499002
SN - 0780385543
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1513
EP - 1516
BT - 2004 International Conference on Image Processing, ICIP 2004
T2 - 2004 International Conference on Image Processing, ICIP 2004
Y2 - 18 October 2004 through 21 October 2004
ER -