IEEE Trans Image Process - Wavelet modeling using finite mixtures of generalized gaussian distributions: application to texture discrimination and retrieval.

Tópicos

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Resumo

This paper addresses statistical-based texture modeling using wavelets. We propose a new approach to represent the marginal distribution of the wavelet coefficients using finite mixtures of generalized Gaussian (MoGG) distributions. The MoGG captures a wide range of histogram shapes, which provides better description and discrimination of texture than using single probability density functions (pdf's), as proposed by recent state-of-the-art approaches. Moreover, we propose a model similarity measure based on Kullback-Leibler divergence (KLD) approximation using Monte Carlo sampling methods. Through experiments on two popular texture data sets, we show that our approach yields significant performance improvements for texture discrimination and retrieval, as compared with recent methods of statistical-based wavelet modeling.

Resumo Limpo

paper address statisticalbas textur model use wavelet propos new approach repres margin distribut wavelet coeffici use finit mixtur general gaussian mogg distribut mogg captur wide rang histogram shape provid better descript discrimin textur use singl probabl densiti function pdfs propos recent stateoftheart approach moreov propos model similar measur base kullbackleibl diverg kld approxim use mont carlo sampl method experi two popular textur data set show approach yield signific perform improv textur discrimin retriev compar recent method statisticalbas wavelet model

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