de Recherche et d’Innovation
en Cybersécurité et Société
Allili, M. S.
Wavelet modeling using finite mixtures of generalized Gaussian distributions: Application to texture discrimination and retrieval Article de journal
Dans: IEEE Transactions on Image Processing, vol. 21, no 4, p. 1452–1464, 2012, ISSN: 10577149.
Résumé | Liens | BibTeX | Étiquettes: algorithm, Algorithms, article, Automated, automated pattern recognition, computer assisted diagnosis, Computer Simulation, Computer-Assisted, Data Interpretation, Finite mixtures, Generalized Gaussian, Generalized Gaussian Distributions, Image Enhancement, Image Interpretation, Image segmentation, Imaging, Kullback Leibler divergence, Marginal distribution, methodology, Mixtures, Models, Monte Carlo methods, Monte Carlo sampling, Normal Distribution, Pattern Recognition, Performance improvements, reproducibility, Reproducibility of Results, Sensitivity and Specificity, Similarity measure, State-of-the-art approach, Statistical, statistical analysis, statistical model, Texture data set, Texture discrimination, Texture modeling, Textures, three dimensional imaging, Three-Dimensional, Wavelet Analysis, Wavelet coefficients, Wavelet decomposition, Wavelet modeling
@article{allili_wavelet_2012,
title = {Wavelet modeling using finite mixtures of generalized Gaussian distributions: Application to texture discrimination and retrieval},
author = {M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84859096106&doi=10.1109%2fTIP.2011.2170701&partnerID=40&md5=0420facdc04978ad84bea3126bc1183a},
doi = {10.1109/TIP.2011.2170701},
issn = {10577149},
year = {2012},
date = {2012-01-01},
journal = {IEEE Transactions on Image Processing},
volume = {21},
number = {4},
pages = {1452–1464},
abstract = {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. © 2011 IEEE.},
keywords = {algorithm, Algorithms, article, Automated, automated pattern recognition, computer assisted diagnosis, Computer Simulation, Computer-Assisted, Data Interpretation, Finite mixtures, Generalized Gaussian, Generalized Gaussian Distributions, Image Enhancement, Image Interpretation, Image segmentation, Imaging, Kullback Leibler divergence, Marginal distribution, methodology, Mixtures, Models, Monte Carlo methods, Monte Carlo sampling, Normal Distribution, Pattern Recognition, Performance improvements, reproducibility, Reproducibility of Results, Sensitivity and Specificity, Similarity measure, State-of-the-art approach, Statistical, statistical analysis, statistical model, Texture data set, Texture discrimination, Texture modeling, Textures, three dimensional imaging, Three-Dimensional, Wavelet Analysis, Wavelet coefficients, Wavelet decomposition, Wavelet modeling},
pubstate = {published},
tppubtype = {article}
}
Allili, M. S.; Ziou, D.; Bouguila, N.; Boutemedjet, S.
Image and video segmentation by combining unsupervised generalized Gaussian mixture modeling and feature selection Article de journal
Dans: IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no 10, p. 1373–1377, 2010, ISSN: 10518215.
Résumé | Liens | BibTeX | Étiquettes: Clustering model, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Heavy-tailed, High dimensional spaces, Image and video segmentation, Image segmentation, image/video segmentation, Minimum message lengths, Real-world image, Video cameras
@article{allili_image_2010,
title = {Image and video segmentation by combining unsupervised generalized Gaussian mixture modeling and feature selection},
author = {M. S. Allili and D. Ziou and N. Bouguila and S. Boutemedjet},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957964550&doi=10.1109%2fTCSVT.2010.2077483&partnerID=40&md5=d888c7fe52eff37a5744bccd6a4d3d9e},
doi = {10.1109/TCSVT.2010.2077483},
issn = {10518215},
year = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
volume = {20},
number = {10},
pages = {1373–1377},
abstract = {In this letter, we propose a clustering model that efficiently mitigates image and video under/over-segmentation by combining generalized Gaussian mixture modeling and feature selection. The model has flexibility to accurately represent heavy-tailed image/video histograms, while automatically discarding uninformative features, leading to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a database of real-world images and videos showed us the effectiveness of the proposed approach. © 2010 IEEE.},
keywords = {Clustering model, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Heavy-tailed, High dimensional spaces, Image and video segmentation, Image segmentation, image/video segmentation, Minimum message lengths, Real-world image, Video cameras},
pubstate = {published},
tppubtype = {article}
}
Allili, M. S.; Ziou, D.; Bouguila, N.; Boutemedjet, S.
Unsupervised feature selection and learning for image segmentation Article d'actes
Dans: CRV 2010 - 7th Canadian Conference on Computer and Robot Vision, p. 285–292, Ottawa, ON, 2010, ISBN: 978-0-7695-4040-5.
Résumé | Liens | BibTeX | Étiquettes: Clustering algorithms, Computer vision, Evolutionary algorithms, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Generalized Gaussian Distributions, Heavy-tailed, High dimensional spaces, Image distributions, Image segmentation, Large database, Over-estimation, Real-world image, Unsupervised feature selection
@inproceedings{allili_unsupervised_2010,
title = {Unsupervised feature selection and learning for image segmentation},
author = {M. S. Allili and D. Ziou and N. Bouguila and S. Boutemedjet},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954407977&doi=10.1109%2fCRV.2010.44&partnerID=40&md5=a7d8e3147216429f18ef7af3167acb42},
doi = {10.1109/CRV.2010.44},
isbn = {978-0-7695-4040-5},
year = {2010},
date = {2010-01-01},
booktitle = {CRV 2010 - 7th Canadian Conference on Computer and Robot Vision},
pages = {285–292},
address = {Ottawa, ON},
abstract = {In this paper we investigate the integration of feature selection in segmentation through an unsupervised learning approach. We propose a clustering algorithm that efficiently mitigates image under/over-segmentation, by combining generalized Gaussian mixture modeling and feature selection. The algorithm is based on generalized Gaussian mixture modeling which is less prone to region number over-estimation in case of noisy and heavy-tailed image distributions. On the other hand, our feature selection mechanism allows to automatically discard uninformative features, which leads to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a large database of real-world images showed us the effectiveness of the proposed approach. © 2010 IEEE.},
keywords = {Clustering algorithms, Computer vision, Evolutionary algorithms, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Generalized Gaussian Distributions, Heavy-tailed, High dimensional spaces, Image distributions, Image segmentation, Large database, Over-estimation, Real-world image, Unsupervised feature selection},
pubstate = {published},
tppubtype = {inproceedings}
}