@article{nouboukpo_spatially-coherent_2019,

title = {Spatially-coherent segmentation using hierarchical gaussian mixture reduction based on cauchy-schwarz divergence},

author = {A. Nouboukpo and M. S. Allili},

editor = {Campilho A. Yu A. Karray F.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071452890&doi=10.1007%2f978-3-030-27202-9_35&partnerID=40&md5=2689080f7b2410040a038f080ef93bfa},

doi = {10.1007/978-3-030-27202-9_35},

issn = {03029743},

year  = {2019},

date = {2019-01-01},

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

volume = {11662 LNCS},

pages = {388–396},

abstract = {Gaussian mixture models (GMM) are widely used for image segmentation. The bigger the number in the mixture, the higher will be the data likelihood. Unfortunately, too many GMM components leads to model overfitting and poor segmentation. Thus, there has been a growing interest in GMM reduction algorithms that rely on component fusion while preserving the structure of data. In this work, we present an algorithm based on a closed-form Cauchy-Schwarz divergence for GMM reduction. Contrarily to previous GMM reduction techniques which a single GMM, our approach can lead to multiple small GMMs describing more accurately the structure of the data. Experiments on image foreground segmentation demonstrate the effectiveness of our proposed model compared to state-of-art methods. © Springer Nature Switzerland AG 2019.},

note = {ISBN: 9783030272012 

Publisher: Springer Verlag},

keywords = {Cauchy-Schwarz divergence, Foreground segmentation, Gaussian distribution, Gaussian Mixture Model, Gaussian mixture reduction, Image analysis, Image segmentation, Mixture reductions, Reduction algorithms, Reduction techniques, State-of-art methods},

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_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{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}

}

@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}

}