Template learning using wavelet domain statistical models

Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Andreas Spanias

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

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationResearch and Development in Intelligent Systems XXVI
Subtitle of host publicationIncorporating Applications and Innovations in Intelligent Systems XVII
Pages179-192
Number of pages14
DOIs
StatePublished - Dec 1 2010
Event29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2009 - Cambridge, United Kingdom
Duration: Dec 15 2009Dec 17 2009

Publication series

NameResearch and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII

Other

Other29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2009
CountryUnited Kingdom
CityCambridge
Period12/15/0912/17/09

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Ramamurthy, K. N., Thiagarajan, J. J., & Spanias, A. (2010). Template learning using wavelet domain statistical models. In Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII (pp. 179-192). (Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII). https://doi.org/10.1007/978-1-84882-983-1-13