@inproceedings{allili_wavelet-based_2010,

title = {Wavelet-based texture retrieval using a mixture of generalized Gaussian distributions},

author = {M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78149489822&doi=10.1109%2fICPR.2010.769&partnerID=40&md5=bf29f6057b57f85a0d83ac16bb4afaf5},

doi = {10.1109/ICPR.2010.769},

isbn = {978-0-7695-4109-9},

year  = {2010},

date = {2010-01-01},

booktitle = {Proceedings - International Conference on Pattern Recognition},

pages = {3143–3146},

address = {Istanbul},

abstract = {In this paper, we address the texture retrieval problem using wavelet distribution. We propose a new statistical scheme to represent the marginal distribution of the wavelet coefficients using a mixture of generalized Gaussian distributions (MoGG). The MoGG allows to capture a wide range of histogram shapes, which provides a better description of texture and enhances texture discrimination. We propose a similarity measurement based on Kullback-Leibler distance (KLD), which is calculated using MCMC Metropolis-Hastings sampling algorithm. We show that our approach yields better texture retrieval results than previous methods using only a single probability density function (pdf) for wavelet representation, or texture energy distribution. © 2010 IEEE.},

note = {ISSN: 10514651},

keywords = {Avelet decomposition, Gaussian distribution, Generalized Gaussian Distributions, Image retrieval, KLD, Kullback-Leibler distance, Marginal distribution, Metropolis-Hastings samplings, Mixtures, Pattern Recognition, Probability density function, Probability density function (pdf), Similarity measurements, Statistical methods, Statistical scheme, Texture discrimination, Texture energy, Texture image retrieval, Texture retrieval, Textures, Wavelet coefficients, Wavelet representation},

pubstate = {published},

tppubtype = {inproceedings}

}