TY - GEN
T1 - Template learning using wavelet domain statistical models
AU - Ramamurthy, Karthikeyan Natesan
AU - Thiagarajan, Jayaraman J.
AU - Spanias, Andreas
PY - 2010
Y1 - 2010
N2 - Wavelets have been used with great success in applications such as signal denoising, compression, estimation and feature extraction. This is because of their ability to capture singularities in the signal with a few coefficients. Applications that consider the statistical dependencies of wavelet coefficients have been shown to perform better than those which assume the wavelet coefficients as independent. In this paper, a novel Gaussian mixture model, specifically suited for template learning is proposed for modeling the marginal statistics of the wavelet coefficients. A Bayesian approach for inferring a low dimensional statistical template with a set of training images, using the independent mixture and the hidden Markov tree models extended to the template learning case, is developed. Results obtained for template learning and pattern classification using the low dimensional templates are presented. For training with a large data set, statistical templates generated using the proposed Bayesian approach are more robust than those generated using an information-theoretic framework in the wavelet domain.
AB - Wavelets have been used with great success in applications such as signal denoising, compression, estimation and feature extraction. This is because of their ability to capture singularities in the signal with a few coefficients. Applications that consider the statistical dependencies of wavelet coefficients have been shown to perform better than those which assume the wavelet coefficients as independent. In this paper, a novel Gaussian mixture model, specifically suited for template learning is proposed for modeling the marginal statistics of the wavelet coefficients. A Bayesian approach for inferring a low dimensional statistical template with a set of training images, using the independent mixture and the hidden Markov tree models extended to the template learning case, is developed. Results obtained for template learning and pattern classification using the low dimensional templates are presented. For training with a large data set, statistical templates generated using the proposed Bayesian approach are more robust than those generated using an information-theoretic framework in the wavelet domain.
UR - http://www.scopus.com/inward/record.url?scp=84882257154&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84882257154&partnerID=8YFLogxK
U2 - 10.1007/978-1-84882-983-1_13
DO - 10.1007/978-1-84882-983-1_13
M3 - Conference contribution
AN - SCOPUS:84882257154
SN - 9781848829824
T3 - Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII
SP - 179
EP - 192
BT - Research and Development in Intelligent Systems XXVI
PB - Springer London
T2 - 29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2009
Y2 - 15 December 2009 through 17 December 2009
ER -