@article{allili_texture_2014,

title = {Texture modeling using contourlets and finite mixtures of generalized gaussian distributions and applications},

author = {M. S. Allili and N. Baaziz and M. Mejri},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896903467&doi=10.1109%2fTMM.2014.2298832&partnerID=40&md5=16b2fa741e71e1e581f6b0f54c43a676},

doi = {10.1109/TMM.2014.2298832},

issn = {15209210},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Multimedia},

volume = {16},

number = {3},

pages = {772–784},

abstract = {In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions (MoGG). On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT, which describes texture structures more accurately. On the other hand, we use MoGG modeling of contourlet coefficients distribution, which allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions (pdfs). Moreover, we propose three applications for the proposed approach, namely: (1) texture retrieval, (2) fabric texture defect detection, and 3) infrared (IR) face recognition. We compare two implementations of the CT: standard CT (SCT) and redundant CT (RCT). We show that the proposed approach yields better results in the applications studied compared to recent state-of-the-art methods. © 2014 IEEE.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {Contourlet coefficients, Contourlet transform, Defects, Directional information, Fabric texture, face recognition, Generalized Gaussian Distributions, Inspection, Mixtures, Probability density function, Probability density functions (PDFs), State-of-the-art methods, Texture retrieval, Textures},

pubstate = {published},

tppubtype = {article}

}

@article{boulmerka_generalized_2014,

title = {A generalized multiclass histogram thresholding approach based on mixture modelling},

author = {A. Boulmerka and M. Saïd Allili and S. Ait-Aoudia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888328869&doi=10.1016%2fj.patcog.2013.09.004&partnerID=40&md5=d8b872bd0abe9e6c4d52439f8ec360bc},

doi = {10.1016/j.patcog.2013.09.004},

issn = {00313203},

year  = {2014},

date = {2014-01-01},

journal = {Pattern Recognition},

volume = {47},

number = {3},

pages = {1330–1348},

abstract = {This paper presents a new approach to multi-class thresholding-based segmentation. It considerably improves existing thresholding methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions using mixtures of generalized Gaussian distributions (MoGG). The proposed approach seamlessly: (1) extends the standard Otsu's method to arbitrary numbers of thresholds and (2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal class-conditional data. MoGGs enable efficient representation of heavy-tailed data and multi-modal histograms with flat or sharply shaped peaks. Experiments on synthetic data and real-world image segmentation show the performance of the proposed approach with comparison to recent state-of-the-art techniques. © 2013 Elsevier Ltd. All rights reserved.},

keywords = {Arbitrary number, Conditional distribution, Gaussian distribution, Gaussian noise (electronic), Generalized Gaussian Distributions, Graphic methods, Histogram thresholding, Image segmentation, Minimum error thresholding, Mixture-modelling, Mixtures, State-of-the-art techniques, Statistical methods, Thresholding, Thresholding methods},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{allili_contourlet-based_2011,

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

author = {M. S. Allili and N. Baaziz},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052796353&doi=10.1007%2f978-3-642-23678-5_53&partnerID=40&md5=fde8aaeea1609c81747b0ab27a8c78ce},

doi = {10.1007/978-3-642-23678-5_53},

issn = {03029743},

year  = {2011},

date = {2011-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {6855 LNCS},

number = {PART 2},

pages = {446–454},

abstract = {We address the texture retrieval problem using contourlet-based statistical representation. We propose a new contourlet distribution modelling using finite mixtures of generalized Gaussian distributions (MoGG). The MoGG allows to capture a wide range of contourlet histogram shapes, which provides better description and discrimination of texture than using single probability density functions (pdfs). We propose a model similarity measure based on Kullback-Leibler divergence (KLD) approximation using Monte-Carlo sampling methods. We show that our approach using a redundant contourlet transform yields better texture discrimination and retrieval results than using other methods of statistical-based wavelet/contourlet modelling. © 2011 Springer-Verlag.},

note = {ISBN: 9783642236778 

Place: Seville},

keywords = {Contourlet transform, Contourlets, Distribution modelling, Finite mixtures, Gaussian distribution, Generalized Gaussian Distributions, Image analysis, Kullback-Leibler divergence, Mixtures, Monte-Carlo sampling, Probability density function, Similarity measure, Statistical representations, Texture discrimination, Texture retrieval, Textures},

pubstate = {published},

tppubtype = {article}

}

@article{ziou_finite_2009,

title = {Finite Gamma mixture modelling using minimum message length inference: Application to SAR image analysis},

author = {D. Ziou and N. Bouguila and M. S. Allili and A. El-Zaart},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-67650686123&doi=10.1080%2f01431160802392646&partnerID=40&md5=901ea39ad806dcb62cd630585469af60},

doi = {10.1080/01431160802392646},

issn = {01431161},

year  = {2009},

date = {2009-01-01},

journal = {International Journal of Remote Sensing},

volume = {30},

number = {3},

pages = {771–792},

abstract = {This paper discusses the unsupervised learning problem for finite mixtures of Gamma distributions. An important part of this problem is determining the number of clusters which best describes a set of data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem in the case of finite mixtures of Gamma distributions. The MML and other criteria in the literature are compared in terms of their ability to estimate the number of clusters in a data set. The comparison utilizes synthetic and RADARSAT SAR images. The performance of our method is also tested by contextual evaluations involving SAR image segmentation and change detection.},

note = {Publisher: Taylor and Francis Ltd.},

keywords = {Change detection, Determining the number of clusters, estimation method, finite element method, Finite mixtures, Gamma distribution, Gamma mixtures, Image analysis, Image processing, Image segmentation, Minimum message lengths, Mixtures, Number of clusters, numerical model, Probability distributions, Radar imaging, SAR image segmentation, Synthetic aperture radar, Unsupervised learning},

pubstate = {published},

tppubtype = {article}

}

@article{allili_finite_2008,

title = {Finite general Gaussian mixture modeling and application to image and video foreground segmentation},

author = {M. S. Allili and N. Bouguila and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78149489170&doi=10.1117%2f1.2898125&partnerID=40&md5=8a9b3060dda2366f17b22a06606a9f09},

doi = {10.1117/1.2898125},

issn = {10179909},

year  = {2008},

date = {2008-01-01},

journal = {Journal of Electronic Imaging},

volume = {17},

number = {1},

abstract = {We propose a new finite mixture model based on the formalism of general Gaussian distribution (GGD). Because it has the flexibility to adapt to the shape of the data better than the Gaussian, the GGD is less prone to overfitting the number of mixture classes when dealing with noisy data. In the first part of this work, we propose a derivation of the maximum likelihood estimation for the parameters of the new mixture model, and elaborate an information-theoretic approach for the selection of the number of classes. In the second part, we validate the proposed model by comparing it to the Gaussian mixture in applications related to image and video foreground segmentation © 2008 SPIE and IS&T.},

keywords = {Finite mixture models, Foreground segmentation, Gaussian distribution, Gaussian mixture modeling, Gaussian mixtures, Gaussians, General Gaussian distribution, Image segmentation, Information theory, Information-theoretic approach, Maximum likelihood estimation, Mixture model, Mixtures, Noisy data, Overfitting},

pubstate = {published},

tppubtype = {article}

}