Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Yapi, D.; Nouboukpo, A.; Allili, M. S.; Member, IEEE
Mixture of multivariate generalized Gaussians for multi-band texture modeling and representation Article de journal
Dans: Signal Processing, vol. 209, 2023, ISSN: 01651684, (Publisher: Elsevier B.V.).
Résumé | Liens | BibTeX | Étiquettes: Color texture retrieval, Content-based, Content-based color-texture retrieval, Convolution, convolutional neural network, Gaussians, Image retrieval, Image texture, Mixture of multivariate generalized gaussians, Multi-scale Decomposition, Subbands, Texture representation, Textures
@article{yapi_mixture_2023,
title = {Mixture of multivariate generalized Gaussians for multi-band texture modeling and representation},
author = {D. Yapi and A. Nouboukpo and M. S. Allili and IEEE Member},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151300047&doi=10.1016%2fj.sigpro.2023.109011&partnerID=40&md5=3bf98e9667eb7b60cb3f59ed1dcb029c},
doi = {10.1016/j.sigpro.2023.109011},
issn = {01651684},
year = {2023},
date = {2023-01-01},
journal = {Signal Processing},
volume = {209},
abstract = {We present a unified statistical model for multivariate and multi-modal texture representation. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables to build a compact and accurate representation of texture images using multi-resolution texture transforms. The MoMGG model enables to describe the joint statistics of subbands in different scales and orientations, as well as between adjacent locations within the same subband, providing a precise description of the texture layout. It can also combine different multi-scale transforms to build a richer and more representative texture signature for image similarity measurement. We tested our model on both traditional texture transforms (e.g., wavelets, contourlets, maximum response filter) and convolution neural networks (CNNs) features (e.g., ResNet, SqueezeNet). Experiments on color-texture image retrieval have demonstrated the performance of our approach comparatively to state-of-the-art methods. © 2023},
note = {Publisher: Elsevier B.V.},
keywords = {Color texture retrieval, Content-based, Content-based color-texture retrieval, Convolution, convolutional neural network, Gaussians, Image retrieval, Image texture, Mixture of multivariate generalized gaussians, Multi-scale Decomposition, Subbands, Texture representation, Textures},
pubstate = {published},
tppubtype = {article}
}
We present a unified statistical model for multivariate and multi-modal texture representation. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables to build a compact and accurate representation of texture images using multi-resolution texture transforms. The MoMGG model enables to describe the joint statistics of subbands in different scales and orientations, as well as between adjacent locations within the same subband, providing a precise description of the texture layout. It can also combine different multi-scale transforms to build a richer and more representative texture signature for image similarity measurement. We tested our model on both traditional texture transforms (e.g., wavelets, contourlets, maximum response filter) and convolution neural networks (CNNs) features (e.g., ResNet, SqueezeNet). Experiments on color-texture image retrieval have demonstrated the performance of our approach comparatively to state-of-the-art methods. © 2023
2.
Yapi, D.; Allili, M. S.
Multi-Band Texture Modeling Using Finite Mixtures of Multivariate Generalized Gaussian Distributions Article d'actes
Dans: Proceedings - International Conference on Pattern Recognition, p. 464–469, Institute of Electrical and Electronics Engineers Inc., 2022, ISBN: 978-1-66549-062-7, (ISSN: 10514651).
Résumé | Liens | BibTeX | Étiquettes: Color texture retrieval, Finite mixtures, Gaussian distribution, Gaussians, Image retrieval, Image texture, Mixture of multivariate generalized gaussians, Multi band, Multi-scale Decomposition, Multivariate generalized gaussian distributions, Statistic modeling, Subbands, Texture models, Textures
@inproceedings{yapi_multi-band_2022,
title = {Multi-Band Texture Modeling Using Finite Mixtures of Multivariate Generalized Gaussian Distributions},
author = {D. Yapi and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143638804&doi=10.1109%2fICPR56361.2022.9956448&partnerID=40&md5=3fe34da98f314b85014059630e4c8c6c},
doi = {10.1109/ICPR56361.2022.9956448},
isbn = {978-1-66549-062-7},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
volume = {2022-August},
pages = {464–469},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {We present a unified statistical model for multivariate and multi-modal texture representation. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables a compact and accurate representation of joint statistics of different sub-bands of multireslotion texture transform. This representation expresses correlation between sub-bands at different scales and orientations, and also between adjacent locations within the same subbands, providing a precise description of the texture layout. It enables also to combine different multi-scale transforms to build a richer and more representative texture signature. We successfully tested the model on traditional texture transforms such as wavelets and contourlets. Experiments on color-texture image retrieval have demonstrated the performance of our approach comparatively to state-of-art methods. © 2022 IEEE.},
note = {ISSN: 10514651},
keywords = {Color texture retrieval, Finite mixtures, Gaussian distribution, Gaussians, Image retrieval, Image texture, Mixture of multivariate generalized gaussians, Multi band, Multi-scale Decomposition, Multivariate generalized gaussian distributions, Statistic modeling, Subbands, Texture models, Textures},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a unified statistical model for multivariate and multi-modal texture representation. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables a compact and accurate representation of joint statistics of different sub-bands of multireslotion texture transform. This representation expresses correlation between sub-bands at different scales and orientations, and also between adjacent locations within the same subbands, providing a precise description of the texture layout. It enables also to combine different multi-scale transforms to build a richer and more representative texture signature. We successfully tested the model on traditional texture transforms such as wavelets and contourlets. Experiments on color-texture image retrieval have demonstrated the performance of our approach comparatively to state-of-art methods. © 2022 IEEE.
3.
Yapi, D.; Mejri, M.; Allili, M. S.; Baaziz, N.
A learning-based approach for automatic defect detection in textile images Article d'actes
Dans: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, p. 2423–2428, 2015, ISBN: 24058963 (ISSN), (Issue: 3 Journal Abbreviation: IFAC-PapersOnLine).
Résumé | Liens | BibTeX | Étiquettes: Algorithms, Artificial intelligence, Automatic defect detections, Barium compounds, Bayes Classifier, Computational efficiency, Contourlets, Defect detection, Defect detection algorithm, Defects, Detection problems, Feature extraction, Feature extraction and classification, Gaussians, Image classification, Learning algorithms, Learning systems, Learning-based approach, Machine learning approaches, Mixture of generalized gaussians, Mixtures of generalized Gaussians (MoGG), Textile defect detection, Textile images, Textiles, Textures
@inproceedings{yapi_learning-based_2015,
title = {A learning-based approach for automatic defect detection in textile images},
author = {D. Yapi and M. Mejri and M. S. Allili and N. Baaziz},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953865559&doi=10.1016%2fj.ifacol.2015.06.451&partnerID=40&md5=3dd0ef4c27cbd55700f6511af5f46772},
doi = {10.1016/j.ifacol.2015.06.451},
isbn = {24058963 (ISSN)},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
pages = {2423–2428},
abstract = {This paper addresses the textile defect detection problem using a machine-learning approach. We propose a novel algorithm that uses supervised learning to classify textile textures in defect and non-defect classes based on suitable feature extraction and classification. We use statistical modeling of multi-scale contourlet image decomposition to obtain compact and accurate signatures for texture description. Our defect detection algorithm is based on two phases. In the first phase, using a training set of images, we extract reference defect-free signatures for each textile category. Then, we use the Bayes classifier (BC) to learn signatures of defected and non-defected classes. In the second phase, defects are detected on new images using the trained BC and an appropriate decomposition of images into blocks. Our algorithm has the capability to achieve highly accurate defect detection and localisation in textile textures while ensuring an efficient computational time. Compared to recent state-of-the-art methods, our algorithm has yielded better results on the standard TILDA database. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
note = {Issue: 3
Journal Abbreviation: IFAC-PapersOnLine},
keywords = {Algorithms, Artificial intelligence, Automatic defect detections, Barium compounds, Bayes Classifier, Computational efficiency, Contourlets, Defect detection, Defect detection algorithm, Defects, Detection problems, Feature extraction, Feature extraction and classification, Gaussians, Image classification, Learning algorithms, Learning systems, Learning-based approach, Machine learning approaches, Mixture of generalized gaussians, Mixtures of generalized Gaussians (MoGG), Textile defect detection, Textile images, Textiles, Textures},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper addresses the textile defect detection problem using a machine-learning approach. We propose a novel algorithm that uses supervised learning to classify textile textures in defect and non-defect classes based on suitable feature extraction and classification. We use statistical modeling of multi-scale contourlet image decomposition to obtain compact and accurate signatures for texture description. Our defect detection algorithm is based on two phases. In the first phase, using a training set of images, we extract reference defect-free signatures for each textile category. Then, we use the Bayes classifier (BC) to learn signatures of defected and non-defected classes. In the second phase, defects are detected on new images using the trained BC and an appropriate decomposition of images into blocks. Our algorithm has the capability to achieve highly accurate defect detection and localisation in textile textures while ensuring an efficient computational time. Compared to recent state-of-the-art methods, our algorithm has yielded better results on the standard TILDA database. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
4.
Allili, M. S.; Baaziz, N.; Mejri, M.
Texture modeling using contourlets and finite mixtures of generalized gaussian distributions and applications Article de journal
Dans: IEEE Transactions on Multimedia, vol. 16, no 3, p. 772–784, 2014, ISSN: 15209210, (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Résumé | Liens | BibTeX | Étiquettes: 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
@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}
}
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.
5.
Bouchard, S.; Dumoulin, S.; Talbot, J.; Ledoux, A. -A.; Phillips, J.; Monthuy-Blanc, J.; Labonté-Chartrand, G.; Robillard, G.; Cantamesse, M.; Renaud, P.
Manipulating subjective realism and its impact on presence: Preliminary results on feasibility and neuroanatomical correlates Article de journal
Dans: Interacting with Computers, vol. 24, no 4, p. 227–236, 2012, ISSN: 09535438, (Publisher: Oxford University Press).
Résumé | Liens | BibTeX | Étiquettes: Experimental conditions, Feeling of presences, fMRI, functional magnetic resonance imaging, Functional neuroimaging, Magnetic resonance imaging, Parahippocampus, Statistically significant difference, Subjective realisms, Technological characteristics, Textures, virtual reality
@article{bouchard_manipulating_2012,
title = {Manipulating subjective realism and its impact on presence: Preliminary results on feasibility and neuroanatomical correlates},
author = {S. Bouchard and S. Dumoulin and J. Talbot and A. -A. Ledoux and J. Phillips and J. Monthuy-Blanc and G. Labonté-Chartrand and G. Robillard and M. Cantamesse and P. Renaud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866389210&doi=10.1016%2fj.intcom.2012.04.011&partnerID=40&md5=f5d975e9f3ae33c5f300faaaee1c5ad0},
doi = {10.1016/j.intcom.2012.04.011},
issn = {09535438},
year = {2012},
date = {2012-01-01},
journal = {Interacting with Computers},
volume = {24},
number = {4},
pages = {227–236},
abstract = {The feeling of presence has been shown to be an important concept in several clinical applications of virtual reality. Among the factors influencing presence, realism factors have been examined extensively from the angle of objective realism. Objective realism has been manipulated by altering numerous technological characteristics such as pictorial quality, texture and shading, or by adding more sensory information (i.e.; smell, touch). Much less studied is the subjective (or perceived) realism, the focus of the two pilot studies reported in this article. In Study 1, subjective realism was manipulated in order to assess the impact on the feeling of presence. Method: Presence was measured in 31 adults after two immersions in virtual reality. Participants were immersed in a neutral/irrelevant virtual environment and subsequently subjected to the experimental manipulation. Participants in the experimental condition were falsely led to believe that they were immersed live in real time in a "real" room with a "real" mouse in a cage. In the control condition, participants believed they were immersed in a replica of the nearby room. All participants were actually immersed in the exact same virtual environment. Results: A manipulation check revealed that 80% of the participants believed in the deception. A 2 Times by 2 Conditions repeated measure ANOVA revealed that leading people to believe they were seeing a real environment digitized live in virtual reality increased their feeling of presence compared to the control condition. In Study 2, the same experimental design was used but with simultaneous functional magnetic resonance imaging (fMRI) in order to assess brain areas potentially related to the feeling of presence. fMRI data from five participants were subjected to a within subject fixed effect analysis to verify differences between the experimental immersion (higher presence) and the control immersion (lower presence). Results revealed a statistically significant difference in left and right parahippocampus areas. Conclusion: Results are discussed according to layers of presence and consciousness and the meaning given to experiences occurring in virtual reality. Some suggestions are formulated to target core presence and extended presence. © 2012 British Informatics Society Limited. All rights reserved.},
note = {Publisher: Oxford University Press},
keywords = {Experimental conditions, Feeling of presences, fMRI, functional magnetic resonance imaging, Functional neuroimaging, Magnetic resonance imaging, Parahippocampus, Statistically significant difference, Subjective realisms, Technological characteristics, Textures, virtual reality},
pubstate = {published},
tppubtype = {article}
}
The feeling of presence has been shown to be an important concept in several clinical applications of virtual reality. Among the factors influencing presence, realism factors have been examined extensively from the angle of objective realism. Objective realism has been manipulated by altering numerous technological characteristics such as pictorial quality, texture and shading, or by adding more sensory information (i.e.; smell, touch). Much less studied is the subjective (or perceived) realism, the focus of the two pilot studies reported in this article. In Study 1, subjective realism was manipulated in order to assess the impact on the feeling of presence. Method: Presence was measured in 31 adults after two immersions in virtual reality. Participants were immersed in a neutral/irrelevant virtual environment and subsequently subjected to the experimental manipulation. Participants in the experimental condition were falsely led to believe that they were immersed live in real time in a "real" room with a "real" mouse in a cage. In the control condition, participants believed they were immersed in a replica of the nearby room. All participants were actually immersed in the exact same virtual environment. Results: A manipulation check revealed that 80% of the participants believed in the deception. A 2 Times by 2 Conditions repeated measure ANOVA revealed that leading people to believe they were seeing a real environment digitized live in virtual reality increased their feeling of presence compared to the control condition. In Study 2, the same experimental design was used but with simultaneous functional magnetic resonance imaging (fMRI) in order to assess brain areas potentially related to the feeling of presence. fMRI data from five participants were subjected to a within subject fixed effect analysis to verify differences between the experimental immersion (higher presence) and the control immersion (lower presence). Results revealed a statistically significant difference in left and right parahippocampus areas. Conclusion: Results are discussed according to layers of presence and consciousness and the meaning given to experiences occurring in virtual reality. Some suggestions are formulated to target core presence and extended presence. © 2012 British Informatics Society Limited. All rights reserved.
6.
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}
}
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.
7.
Allili, M. S.; Baaziz, N.
Contourlet-based texture retrieval using a mixture of generalized Gaussian distributions Article de journal
Dans: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6855 LNCS, no PART 2, p. 446–454, 2011, ISSN: 03029743, (ISBN: 9783642236778 Place: Seville).
Résumé | Liens | BibTeX | Étiquettes: 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
@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}
}
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.
8.
Allili, M. S.
Wavelet-based texture retrieval using a mixture of generalized Gaussian distributions Article d'actes
Dans: Proceedings - International Conference on Pattern Recognition, p. 3143–3146, Istanbul, 2010, ISBN: 978-0-7695-4109-9, (ISSN: 10514651).
Résumé | Liens | BibTeX | Étiquettes: 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
@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}
}
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.
9.
Allili, M. S.; Ziou, D.
Automatic colour-texture image segmentation using active contours Article de journal
Dans: International Journal of Computer Mathematics, vol. 84, no 9, p. 1325–1338, 2007, ISSN: 00207160.
Résumé | Liens | BibTeX | Étiquettes: Automatic segmentation, Computation theory, Image segmentation, Optimization, Parameter estimation, Texture image segmentation, Textures
@article{allili_automatic_2007,
title = {Automatic colour-texture image segmentation using active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548354750&doi=10.1080%2f00207160701250501&partnerID=40&md5=69002e599b1c570571b04367ec08d2ac},
doi = {10.1080/00207160701250501},
issn = {00207160},
year = {2007},
date = {2007-01-01},
journal = {International Journal of Computer Mathematics},
volume = {84},
number = {9},
pages = {1325–1338},
abstract = {In this paper we propose a fully automatic segmentation method for colour/texture images. By fully automatic, we mean that the steps of region initialization and calculation of the number of regions are performed automatically by the method. The region information is formulated using a mixture of pdfs for the combination of colour and texture features. The segmentation is obtained by minimizing an energy functional combining boundary and region information, which evolves the initial region contours towards the real region boundaries and adapts the mixture parameters to the region data. The method is implemented using the level sets that permit automatic handling of topology changes and stable numerical schemes. We validate the approach using examples of synthetic and natural colour-texture image segmentation.},
keywords = {Automatic segmentation, Computation theory, Image segmentation, Optimization, Parameter estimation, Texture image segmentation, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper we propose a fully automatic segmentation method for colour/texture images. By fully automatic, we mean that the steps of region initialization and calculation of the number of regions are performed automatically by the method. The region information is formulated using a mixture of pdfs for the combination of colour and texture features. The segmentation is obtained by minimizing an energy functional combining boundary and region information, which evolves the initial region contours towards the real region boundaries and adapts the mixture parameters to the region data. The method is implemented using the level sets that permit automatic handling of topology changes and stable numerical schemes. We validate the approach using examples of synthetic and natural colour-texture image segmentation.
10.
Allili, M. S.; Ziou, D.
Globally adaptive region information for automatic color-texture image segmentation Article de journal
Dans: Pattern Recognition Letters, vol. 28, no 15, p. 1946–1956, 2007, ISSN: 01678655.
Résumé | Liens | BibTeX | Étiquettes: Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures
@article{allili_globally_2007,
title = {Globally adaptive region information for automatic color-texture image segmentation},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548675168&doi=10.1016%2fj.patrec.2007.05.002&partnerID=40&md5=e1338223b9cb99afc35dfbfbf7859b72},
doi = {10.1016/j.patrec.2007.05.002},
issn = {01678655},
year = {2007},
date = {2007-01-01},
journal = {Pattern Recognition Letters},
volume = {28},
number = {15},
pages = {1946–1956},
abstract = {In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.},
keywords = {Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.