Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Gapski, M. C. B.; Kawai, V. A. S.; Leticio, G. R.; Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation Journal Article
In: Journal of the Brazilian Computer Society, vol. 32, no. 1, pp. 1731–1754, 2026, ISSN: 01046500 (ISSN).
Abstract | Links | BibTeX | Tags: Classification accuracy, Convolution, Convolutional neural networks, extraction, Feature extraction, Feature extractor, Feature Fusion, Feature representation, Features fusions, Graph Neural Networks, Graph representation, Graph structures, Graph theory, Graphic methods, Image classification, Labeled data, Rank Aggregation, Semi-supervised, Semi-supervised image classification, Supervised image classifications, Textures
@article{gapskiGraphNeuralNetworks2026,
title = {Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation},
author = {M. C. B. Gapski and V. A. S. Kawai and G. R. Leticio and L. P. Valem and D. C. G. Pedronette and M. S. Allili},
url = {https://www.scopus.com/pages/publications/105045179876?origin=resultslist},
doi = {10.5753/jbcs.2026.5880},
issn = {01046500 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Journal of the Brazilian Computer Society},
volume = {32},
number = {1},
pages = {1731–1754},
publisher = {Brazilian Computing Society},
abstract = {Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes. Contemporary feature extractors predominantly leverage deep learning architectures, such as Convolutional Neural Networks (CNNs) and Vision Transformers (VITs). The availability of diverse feature extractors in the literature provides a wide range of feature representations. Features extracted from an image depend on the specific application, the chosen extractor, and its configuration. Therefore, integrating complementary information by combining distinct extractors offers a promising way to enhance performance. Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), have emerged as powerful and widely adopted approaches for semi-supervised image classification, as they effectively leverage both labeled and unlabeled data while exploiting the underlying graph structures that capture relationships among samples. This study proposes a novel approach for GNNs in scenarios where labeled data is scarce, by integrating diverse sets of feature and graph representations derived from various extractors in classification scenarios. Experimental investigations were conducted, encompassing combinations of distinct feature and graph extractors, as well as rank aggregation strategies. The primary contributions of this work are underscored by the experimental findings, which demonstrate that the strategic combination of feature and graph representations, coupled with the application of manifold learning for graph processing, leads to significant improvements in classification accuracy across the majority of experimental conditions. Furthermore, the utilization of rank aggregation techniques to integrate features from different extractors was shown to enhance classification accuracy. © 2026, Brazilian Computing Society. All rights reserved.},
keywords = {Classification accuracy, Convolution, Convolutional neural networks, extraction, Feature extraction, Feature extractor, Feature Fusion, Feature representation, Features fusions, Graph Neural Networks, Graph representation, Graph structures, Graph theory, Graphic methods, Image classification, Labeled data, Rank Aggregation, Semi-supervised, Semi-supervised image classification, Supervised image classifications, Textures},
pubstate = {published},
tppubtype = {article}
}
Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes. Contemporary feature extractors predominantly leverage deep learning architectures, such as Convolutional Neural Networks (CNNs) and Vision Transformers (VITs). The availability of diverse feature extractors in the literature provides a wide range of feature representations. Features extracted from an image depend on the specific application, the chosen extractor, and its configuration. Therefore, integrating complementary information by combining distinct extractors offers a promising way to enhance performance. Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), have emerged as powerful and widely adopted approaches for semi-supervised image classification, as they effectively leverage both labeled and unlabeled data while exploiting the underlying graph structures that capture relationships among samples. This study proposes a novel approach for GNNs in scenarios where labeled data is scarce, by integrating diverse sets of feature and graph representations derived from various extractors in classification scenarios. Experimental investigations were conducted, encompassing combinations of distinct feature and graph extractors, as well as rank aggregation strategies. The primary contributions of this work are underscored by the experimental findings, which demonstrate that the strategic combination of feature and graph representations, coupled with the application of manifold learning for graph processing, leads to significant improvements in classification accuracy across the majority of experimental conditions. Furthermore, the utilization of rank aggregation techniques to integrate features from different extractors was shown to enhance classification accuracy. © 2026, Brazilian Computing Society. All rights reserved.
2.
Merzouki, K.; Hadi, Y.; Elghazi, H.; Moudoud, H.; Houda, Z. A. El
Graph Neural Network Framework for Advanced Persistent Threat Detection in IIoT Environments Proceedings Article
In: F., El Bouanani; F., Ayoub (Ed.): Int. Conf. Adv. Commun. Technol. Netw., CommNet - Proc., Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833155781-2 (ISBN), (Journal Abbreviation: Int. Conf. Adv. Commun. Technol. Netw., CommNet - Proc.).
Abstract | Links | BibTeX | Tags: Advanced persistent threat, Advanced Persistent Threats, Anomaly detection, CICAPT-IIoT2024, Deep learning, extraction, Feature extraction, Features extraction, Graph Neural Networks, Graphic methods, IIoT, Industrial infrastructure, Industrial internet of thing, Learning systems, Message passing, Network architecture, Network frameworks, Network security, Relational learning, Threat detection
@inproceedings{merzoukiGraphNeuralNetwork2025,
title = {Graph Neural Network Framework for Advanced Persistent Threat Detection in IIoT Environments},
author = {K. Merzouki and Y. Hadi and H. Elghazi and H. Moudoud and Z. A. El Houda},
editor = {El Bouanani F. and Ayoub F.},
url = {https://www.scopus.com/pages/publications/105032074689?origin=resultslist},
doi = {10.1109/CommNet68224.2025.11288886},
isbn = {979-833155781-2 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int. Conf. Adv. Commun. Technol. Netw., CommNet - Proc.},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The increasing reliance of industrial infrastructures on the Industrial Internet of Things (IIoT) has made them more vulnerable to complex cyberattacks, especially Advanced Persistent Threats (APTs). To recognize and analyze these multi-stage incursions, we require computational models that can capture both structural and temporal connections in IIoT network architectures. This paper presents a framework based on Graph Neural Networks (GNNs) for detecting and classifying APTs in IIoT settings. We use the CICAPT-IIoT2024 dataset, which provides realistic multi-phase APT attack scenarios. The approach models system components, network communications, and process interactions as nodes and edges in a dynamic graph, allowing for relational learning and context-aware feature extraction. The GNN architecture leverages graph connectivity patterns and message-passing techniques to identify attack phases with greater accuracy and robustness. Experimental results show that this method outperforms traditional deep learning techniques and ensemble methods, particularly in early-stage anomaly detection. This paper highlights the potential of graph-based learning as an effective way to enhance IIoT infrastructure security against the changing behaviors of advanced persistent threats. © 2025 IEEE.},
note = {Journal Abbreviation: Int. Conf. Adv. Commun. Technol. Netw., CommNet - Proc.},
keywords = {Advanced persistent threat, Advanced Persistent Threats, Anomaly detection, CICAPT-IIoT2024, Deep learning, extraction, Feature extraction, Features extraction, Graph Neural Networks, Graphic methods, IIoT, Industrial infrastructure, Industrial internet of thing, Learning systems, Message passing, Network architecture, Network frameworks, Network security, Relational learning, Threat detection},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing reliance of industrial infrastructures on the Industrial Internet of Things (IIoT) has made them more vulnerable to complex cyberattacks, especially Advanced Persistent Threats (APTs). To recognize and analyze these multi-stage incursions, we require computational models that can capture both structural and temporal connections in IIoT network architectures. This paper presents a framework based on Graph Neural Networks (GNNs) for detecting and classifying APTs in IIoT settings. We use the CICAPT-IIoT2024 dataset, which provides realistic multi-phase APT attack scenarios. The approach models system components, network communications, and process interactions as nodes and edges in a dynamic graph, allowing for relational learning and context-aware feature extraction. The GNN architecture leverages graph connectivity patterns and message-passing techniques to identify attack phases with greater accuracy and robustness. Experimental results show that this method outperforms traditional deep learning techniques and ensemble methods, particularly in early-stage anomaly detection. This paper highlights the potential of graph-based learning as an effective way to enhance IIoT infrastructure security against the changing behaviors of advanced persistent threats. © 2025 IEEE.
3.
Bacha, S.; Allili, M. S.; Benblidia, N.
Event recognition in photo albums using probabilistic graphical model and feature relevance Proceedings Article
In: Proceedings - International Conference on Pattern Recognition, pp. 2819–2823, Institute of Electrical and Electronics Engineers Inc., 2016, ISBN: 10514651 (ISSN); 978-150904847-2 (ISBN), (Journal Abbreviation: Proc. Int. Conf. Pattern Recognit.).
Abstract | Links | BibTeX | Tags: Discriminative power, Event recognition, Feature relevance, Graphic methods, Image features, Object detection, Photo album, Probabilistic graphical models, Probabilistic graphical models (PGM), Semantic event, Semantics, Speech recognition
@inproceedings{bacha_event_2016-1,
title = {Event recognition in photo albums using probabilistic graphical model and feature relevance},
author = {S. Bacha and M. S. Allili and N. Benblidia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019068722&doi=10.1109%2fICPR.2016.7900063&partnerID=40&md5=7aaf779f00590113afa31fde63016619},
doi = {10.1109/ICPR.2016.7900063},
isbn = {10514651 (ISSN); 978-150904847-2 (ISBN)},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
volume = {0},
pages = {2819–2823},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The exponential use of digital cameras has raised a new problem: how to store/retrieve images/albums in very large photo databases that correspond to special events. In this paper, we propose a new probabilistic graphical model (PGM) to recognize events in photo albums stored by users. The PGM combines high-level image features consisting of scenes and objects detected in images. To consider the discriminative power of features, our model integrates the object/scene relevance for more precise prediction of semantic events in photo albums. Experimental results carried out on the challenging PEC dataset with 807 photo albums are presented. © 2016 IEEE.},
note = {Journal Abbreviation: Proc. Int. Conf. Pattern Recognit.},
keywords = {Discriminative power, Event recognition, Feature relevance, Graphic methods, Image features, Object detection, Photo album, Probabilistic graphical models, Probabilistic graphical models (PGM), Semantic event, Semantics, Speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
The exponential use of digital cameras has raised a new problem: how to store/retrieve images/albums in very large photo databases that correspond to special events. In this paper, we propose a new probabilistic graphical model (PGM) to recognize events in photo albums stored by users. The PGM combines high-level image features consisting of scenes and objects detected in images. To consider the discriminative power of features, our model integrates the object/scene relevance for more precise prediction of semantic events in photo albums. Experimental results carried out on the challenging PEC dataset with 807 photo albums are presented. © 2016 IEEE.
4.
Bacha, S.; Allili, M. S.; Benblidia, N.
Event recognition in photo albums using probabilistic graphical models and feature relevance Journal Article
In: Journal of Visual Communication and Image Representation, vol. 40, no. Part B, pp. 546–558, 2016, ISSN: 10473203 (ISSN), (Publisher: Academic Press Inc.).
Abstract | Links | BibTeX | Tags: Event prediction, Event recognition, Feature relevance, Graphic methods, Object/scene relevance, Photo album, Photo albums, Probabilistic graphical models, Probabilistic graphical models (PGM), Speech recognition, Visual feature
@article{bacha_event_2016,
title = {Event recognition in photo albums using probabilistic graphical models and feature relevance},
author = {S. Bacha and M. S. Allili and N. Benblidia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992111208&doi=10.1016%2fj.jvcir.2016.07.021&partnerID=40&md5=20ebb156819c8fcae6e28949046ceb6e},
doi = {10.1016/j.jvcir.2016.07.021},
issn = {10473203 (ISSN)},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
journal = {Journal of Visual Communication and Image Representation},
volume = {40},
number = {Part B},
pages = {546–558},
publisher = {Academic Press Inc.},
abstract = {This paper proposes a method for event recognition in photo albums which aims at predicting the event categories of groups of photos. We propose a probabilistic graphical model (PGM) for event prediction based on high-level visual features consisting of objects and scenes, which are extracted directly from images. For better discrimination between different event categories, we develop a scheme to integrate feature relevance in our model which yields a more powerful inference when album images exhibit a large number of objects and scenes. It allows also to mitigate the influence of non-informative images usually contained in the albums. The performance of the proposed method is validated using extensive experiments on the recently-proposed PEC dataset containing over 61 000 images. Our method obtained the highest accuracy which outperforms previous work. © 2016 Elsevier Inc.},
note = {Publisher: Academic Press Inc.},
keywords = {Event prediction, Event recognition, Feature relevance, Graphic methods, Object/scene relevance, Photo album, Photo albums, Probabilistic graphical models, Probabilistic graphical models (PGM), Speech recognition, Visual feature},
pubstate = {published},
tppubtype = {article}
}
This paper proposes a method for event recognition in photo albums which aims at predicting the event categories of groups of photos. We propose a probabilistic graphical model (PGM) for event prediction based on high-level visual features consisting of objects and scenes, which are extracted directly from images. For better discrimination between different event categories, we develop a scheme to integrate feature relevance in our model which yields a more powerful inference when album images exhibit a large number of objects and scenes. It allows also to mitigate the influence of non-informative images usually contained in the albums. The performance of the proposed method is validated using extensive experiments on the recently-proposed PEC dataset containing over 61 000 images. Our method obtained the highest accuracy which outperforms previous work. © 2016 Elsevier Inc.
5.
Filali, I.; Allili, M. S.; Benblidia, N.
Multi-graph based salient object detection Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9730, pp. 318–324, 2016, ISSN: 03029743, (ISBN: 9783319415000).
Abstract | Links | BibTeX | Tags: Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects
@article{filali_multi-graph_2016,
title = {Multi-graph based salient object detection},
author = {I. Filali and M. S. Allili and N. Benblidia},
editor = {Karray F. Campilho A. Campilho A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978804496&doi=10.1007%2f978-3-319-41501-7_36&partnerID=40&md5=eb519756d2e72245e4131d5dc0b416b5},
doi = {10.1007/978-3-319-41501-7_36},
issn = {03029743},
year = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9730},
pages = {318–324},
publisher = {Springer Verlag},
abstract = {We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.},
note = {ISBN: 9783319415000},
keywords = {Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects},
pubstate = {published},
tppubtype = {article}
}
We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.
6.
Bacha, S.; Allili, M. S.; Benblidia, N.
Event recognition in photo albums using probabilistic graphical models and feature relevance Journal Article
In: Journal of Visual Communication and Image Representation, vol. 40, no. Part B, pp. 546–558, 2016, ISSN: 10473203 (ISSN).
Abstract | Links | BibTeX | Tags: Event prediction, Event recognition, Feature relevance, Graphic methods, Object/scene relevance, Photo album, Photo albums, Probabilistic graphical models, Probabilistic graphical models (PGM), Speech recognition, Visual feature
@article{bachaEventRecognitionPhoto2016a,
title = {Event recognition in photo albums using probabilistic graphical models and feature relevance},
author = {S. Bacha and M. S. Allili and N. Benblidia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992111208&doi=10.1016%2fj.jvcir.2016.07.021&partnerID=40&md5=20ebb156819c8fcae6e28949046ceb6e},
doi = {10.1016/j.jvcir.2016.07.021},
issn = {10473203 (ISSN)},
year = {2016},
date = {2016-01-01},
journal = {Journal of Visual Communication and Image Representation},
volume = {40},
number = {Part B},
pages = {546–558},
publisher = {Academic Press Inc.},
abstract = {This paper proposes a method for event recognition in photo albums which aims at predicting the event categories of groups of photos. We propose a probabilistic graphical model (PGM) for event prediction based on high-level visual features consisting of objects and scenes, which are extracted directly from images. For better discrimination between different event categories, we develop a scheme to integrate feature relevance in our model which yields a more powerful inference when album images exhibit a large number of objects and scenes. It allows also to mitigate the influence of non-informative images usually contained in the albums. The performance of the proposed method is validated using extensive experiments on the recently-proposed PEC dataset containing over 61 000 images. Our method obtained the highest accuracy which outperforms previous work. © 2016 Elsevier Inc.},
keywords = {Event prediction, Event recognition, Feature relevance, Graphic methods, Object/scene relevance, Photo album, Photo albums, Probabilistic graphical models, Probabilistic graphical models (PGM), Speech recognition, Visual feature},
pubstate = {published},
tppubtype = {article}
}
This paper proposes a method for event recognition in photo albums which aims at predicting the event categories of groups of photos. We propose a probabilistic graphical model (PGM) for event prediction based on high-level visual features consisting of objects and scenes, which are extracted directly from images. For better discrimination between different event categories, we develop a scheme to integrate feature relevance in our model which yields a more powerful inference when album images exhibit a large number of objects and scenes. It allows also to mitigate the influence of non-informative images usually contained in the albums. The performance of the proposed method is validated using extensive experiments on the recently-proposed PEC dataset containing over 61 000 images. Our method obtained the highest accuracy which outperforms previous work. © 2016 Elsevier Inc.
7.
Bacha, S.; Allili, M. S.; Benblidia, N.
Event recognition in photo albums using probabilistic graphical model and feature relevance Proceedings Article
In: Proceedings - International Conference on Pattern Recognition, pp. 2819–2823, Institute of Electrical and Electronics Engineers Inc., 2016, ISBN: 10514651 (ISSN); 978-150904847-2 (ISBN), (Journal Abbreviation: Proc. Int. Conf. Pattern Recognit.).
Abstract | Links | BibTeX | Tags: Discriminative power, Event recognition, Feature relevance, Graphic methods, Image features, Object detection, Photo album, Probabilistic graphical models, Probabilistic graphical models (PGM), Semantic event, Semantics, Speech recognition
@inproceedings{bachaEventRecognitionPhoto2016,
title = {Event recognition in photo albums using probabilistic graphical model and feature relevance},
author = {S. Bacha and M. S. Allili and N. Benblidia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019068722&doi=10.1109%2fICPR.2016.7900063&partnerID=40&md5=7aaf779f00590113afa31fde63016619},
doi = {10.1109/ICPR.2016.7900063},
isbn = {10514651 (ISSN); 978-150904847-2 (ISBN)},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
volume = {0},
pages = {2819–2823},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The exponential use of digital cameras has raised a new problem: how to store/retrieve images/albums in very large photo databases that correspond to special events. In this paper, we propose a new probabilistic graphical model (PGM) to recognize events in photo albums stored by users. The PGM combines high-level image features consisting of scenes and objects detected in images. To consider the discriminative power of features, our model integrates the object/scene relevance for more precise prediction of semantic events in photo albums. Experimental results carried out on the challenging PEC dataset with 807 photo albums are presented. © 2016 IEEE.},
note = {Journal Abbreviation: Proc. Int. Conf. Pattern Recognit.},
keywords = {Discriminative power, Event recognition, Feature relevance, Graphic methods, Image features, Object detection, Photo album, Probabilistic graphical models, Probabilistic graphical models (PGM), Semantic event, Semantics, Speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
The exponential use of digital cameras has raised a new problem: how to store/retrieve images/albums in very large photo databases that correspond to special events. In this paper, we propose a new probabilistic graphical model (PGM) to recognize events in photo albums stored by users. The PGM combines high-level image features consisting of scenes and objects detected in images. To consider the discriminative power of features, our model integrates the object/scene relevance for more precise prediction of semantic events in photo albums. Experimental results carried out on the challenging PEC dataset with 807 photo albums are presented. © 2016 IEEE.
8.
Filali, I.; Allili, M. S.; Benblidia, N.
Multi-graph based salient object detection Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9730, pp. 318–324, 2016, ISSN: 03029743, (ISBN: 9783319415000).
Abstract | Links | BibTeX | Tags: Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects
@article{filaliMultigraphBasedSalient2016,
title = {Multi-graph based salient object detection},
author = {I. Filali and M. S. Allili and N. Benblidia},
editor = {Karray F. Campilho A. Campilho A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978804496&doi=10.1007%2f978-3-319-41501-7_36&partnerID=40&md5=eb519756d2e72245e4131d5dc0b416b5},
doi = {10.1007/978-3-319-41501-7_36},
issn = {03029743},
year = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9730},
pages = {318–324},
publisher = {Springer Verlag},
abstract = {We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.},
note = {ISBN: 9783319415000},
keywords = {Graphic methods, Image analysis, Image segmentation, Multi-layer graphs, Multi-scale image decomposition, Multiscale segmentation, Natural images, Object detection, Object recognition, Objective functions, Saliency map, Salient object detection, Salient objects},
pubstate = {published},
tppubtype = {article}
}
We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection. © Springer International Publishing Switzerland 2016.
9.
Boulmerka, A.; Allili, M. Saïd; Ait-Aoudia, S.
A generalized multiclass histogram thresholding approach based on mixture modelling Journal Article
In: Pattern Recognition, vol. 47, no. 3, pp. 1330–1348, 2014, ISSN: 00313203.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
10.
Boulmerka, A.; Allili, M. Saïd; Ait-Aoudia, S.
A generalized multiclass histogram thresholding approach based on mixture modelling Journal Article
In: Pattern Recognition, vol. 47, no. 3, pp. 1330–1348, 2014, ISSN: 00313203.
Abstract | Links | BibTeX | Tags: 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
@article{boulmerkaGeneralizedMulticlassHistogram2014,
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}
}
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.