Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Nouboukpo, A.; Allili, M. S.
Spatially-coherent segmentation using hierarchical gaussian mixture reduction based on cauchy-schwarz divergence Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11662 LNCS, pp. 388–396, 2019, ISSN: 03029743, (ISBN: 9783030272012 Publisher: Springer Verlag).
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
2.
Allili, M. S.; Bouguila, N.; Ziou, D.
Finite general Gaussian mixture modeling and application to image and video foreground segmentation Journal Article
In: Journal of Electronic Imaging, vol. 17, no. 1, 2008, ISSN: 10179909.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
3.
Allili, M. S.; Bouguila, N.; Ziou, D.
Finite generalized Gaussian mixture modeling and applications to image and video foreground segmentation Proceedings Article
In: Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, pp. 183–190, Montreal, QC, 2007, ISBN: 0-7695-2786-8 978-0-7695-2786-4.
Abstract | Links | BibTeX | Tags: Data structures, Finite mixture models, Foreground segmentation, Image segmentation, Information theory, Mathematical models, Maximum likelihood estimation, Mixture of General Gaussions (MoGG)
@inproceedings{allili_finite_2007,
title = {Finite generalized Gaussian mixture modeling and applications 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-s2.0-34548671710&doi=10.1109%2fCRV.2007.33&partnerID=40&md5=be89ffce30db18d0716df9eba2a197a2},
doi = {10.1109/CRV.2007.33},
isbn = {0-7695-2786-8 978-0-7695-2786-4},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007},
pages = {183–190},
address = {Montreal, QC},
abstract = {In this paper, we propose a finite mixture model of generalized Gaussian distributions (GDD) for robust segmentation and data modeling in the presence of noise and outliers. The model has more flexibility to adapt the shape of data and less sensibility for over-fitting the number of classes than the Gaussian mixture. In a first part of the present work, we propose a derivation of the Maximum-Likelihood estimation of the parameters of the new mixture model and we propose an information-theory based approach for the selection of the number of classes. In a second part, we propose some applications relating to image, motion and foreground segmentation to measure the performance of the new model in image data modeling with comparison to the Gaussian mixture. © 2007 IEEE.},
keywords = {Data structures, Finite mixture models, Foreground segmentation, Image segmentation, Information theory, Mathematical models, Maximum likelihood estimation, Mixture of General Gaussions (MoGG)},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a finite mixture model of generalized Gaussian distributions (GDD) for robust segmentation and data modeling in the presence of noise and outliers. The model has more flexibility to adapt the shape of data and less sensibility for over-fitting the number of classes than the Gaussian mixture. In a first part of the present work, we propose a derivation of the Maximum-Likelihood estimation of the parameters of the new mixture model and we propose an information-theory based approach for the selection of the number of classes. In a second part, we propose some applications relating to image, motion and foreground segmentation to measure the performance of the new model in image data modeling with comparison to the Gaussian mixture. © 2007 IEEE.