Ouiem Bchir - Academia.edu (original) (raw)

Papers by Ouiem Bchir

Pattern Analysis and Applications, Mar 21, 2015

We propose a new relational clustering approach, called Fuzzy clustering with Learnable Cluster d... more We propose a new relational clustering approach, called Fuzzy clustering with Learnable Cluster dependent Kernels (FLeCK), that learns multiple kernels while seeking compact clusters. A Gaussian kernel is learned with respect to each cluster. It reflects the relative density, size, and position of the cluster with respect to the other clusters. These kernels are learned by optimizing both the intra-cluster and the intercluster similarities. Moreover, FLeCK is formulated to work on relational data. This makes it applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. The experiments show that FLeCK outperforms several other algorithms. In particular, we show that when data include clusters with various inter and intra cluster distances, learning cluster dependent kernel is crucial in obtaining a good partition.

International Journal of Advanced Computer Science and Applications, 2022

Communication is a critical skill for humans. People who have been deprived from communicating th... more Communication is a critical skill for humans. People who have been deprived from communicating through words like the rest of humans, usually use sign language. For sign language, the main signs features are the handshape, the location, the movement, the orientation and the non-manual component. The vast spread of mobile phones presents an opportunity for hearing-disabled people to engage more into their communities. Designing and implementing a novel Arabic Sign Language (ArSL) recognition system would significantly affect their quality of life. Deep learning models are usually heavy for mobile phones. The more layers a neural network has, the heavier it is. However, typical deep neural network necessitates a large number of layers to attain adequate classification performance. This project aims at addressing the Arabic Sign Language recognition problem and ensuring a trade-off between optimizing the classification performance and scaling down the architecture of the deep network to reduce the computational cost. Specifically, we adapted Efficient Network (EfficientNet) models and generated lightweight deep learning models to classify Arabic Sign Language gestures. Furthermore, a real dataset collected by many different signers to perform hand gestures for thirty different Arabic alphabets. Then, an appropriate performance metrics used in order to assess the classification outcomes obtained by the proposed lightweight models. Besides, preprocessing and data augmentation techniques were investigated to enhance the models generalization. The best results were obtained using the EfficientNet-Lite 0 architecture and the Label smooth as loss function. Our model achieved 94% and proved to be effective against background variations.

Applied sciences, Nov 18, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Applied Sciences

Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learn... more Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing semi-supervised clustering approaches are relatively scarce compared to the contributions made to enhance the state-of-the-art fully unsupervised clustering approaches. In this paper, we propose a novel semi-supervised deep clustering approach, named Soft Constrained Deep Clustering (SC-DEC), that aims to address the limitations exhibited by existing semi-supervised clustering approaches. Specifically, the proposed approach leverages a deep neural network architecture and generates fuzzy membership degrees that better reflect the true partition of the data. In particular, the proposed approach uses side-information and formulates it as a set of soft pairwise constraints to supervise the machine learning process. This supervision information is ex...

This paper reviews multiple query approaches for Content-Based Image Retrieval systems (MQIR). Th... more This paper reviews multiple query approaches for Content-Based Image Retrieval systems (MQIR). These are recently proposed Content-Based Image Retrieval systems that enhance the retrieval performance by conveying a richer understanding of the user high-level interest to the retrieval system. In fact, by allowing the user to express his interest using a set of query images, MQIR bridge the semantic gap with the low-level image features. Nevertheless, the main challenge of MQRI systems is how to compute the distances between the set of query images and each image in the database in a way that enhances the retrieval results and reflects the highlevel semantic the user is interested in. For this matter, several approaches have been reported in the literature. In this paper, we investigate existing multiple query retrieval systems. We describe each approach, detail the way it computes the distances between the set of query images and each image in the database, and analyze its advantages...

International Journal of Advanced Computer Science and Applications, 2021

Clustering is a fundamental problem in machine learning. To address this, a large number of algor... more Clustering is a fundamental problem in machine learning. To address this, a large number of algorithms have been developed. Some of these algorithms, such as K-means, handle the original data directly, while others, such as spectral clustering, apply linear transformation to the data. Still others, such as kernel-based algorithms, use nonlinear transformation. Since the performance of the clustering depends strongly on the quality of the data representation, representation learning approaches have been extensively researched. With the recent advances in deep learning, deep neural networks are being increasingly utilized to learn clustering-friendly representation. We provide here a review of existing algorithms that are being used to jointly optimize deep neural networks and clustering methods.

International Journal of Advanced Computer Science and Applications, 2020

In this paper, we propose a Computer Aided Diagnosis (CAD) system in order to assist the physicia... more In this paper, we propose a Computer Aided Diagnosis (CAD) system in order to assist the physicians in the early detection of Alzheimer's Disease (AD) and ensure an effective diagnosis. The proposed framework is designed to be fully-automated upon the capture of the brain structure using Magnetic Resonance Imaging (MRI) scanners. The Voxel-Based Morphometry (VBM) analysis is a key element in the proposed detection process as it is intended to investigate the Gray Matter (GM) tissues in the captured MRI images. In other words, the feature extraction phase consists in encoding the voxel properties in the MRI images into numerical vectors. The resulting feature vectors are then fed into a Neighborhood Component Analysis and Feature Selection (NCFS) algorithm coupled with K-Nearest Neighbor (KNN) algorithm in order to learn a classification model able to recognize AD cases. The feature selection based on NCFS algorithm improved the overall classification performance.

Journal of Computer Science, 2020

As the number of fall incidents among elderly people and patients are continuously growing, resea... more As the number of fall incidents among elderly people and patients are continuously growing, researches boosted their researches to propose efficient automatic fall detection systems. In particular, they formulated the fall detection problem as a supervised learning task where some visual features are extracted from the video frames and used to automatically identify the position of a human as "Fall" or "Non-Fall" based on a model learned using labeled training frames. Despite the promising reported results, existing fall detection systems exhibit noticeable room for improvement. Learner fusion which builds multiple models and aggregates their respective decisions is an alternative that would improve the fall detection performance. In this paper, an image-based fall detection system that captures the visual property and the spatial position of the human body using the Histogram of Oriented Gradient from the video frames is proposed. Then, the extracted features are used to train three classification models. Namely, the Naïve Bayes, the K-Nearest Neighbors and the Support Vector Machine algorithms are adopted. Next, the majority vote is used to aggregate the decisions of the individual learners. The proposed system was assessed using a standard dataset and yielded promising results. Standard performance measures along with the statistical significance t-test were used to prove that the fall detection system based on majority vote fusion outperforms the individual classifier based approaches.

Journal of Computer Science and Technology, 2017

Building and Environment, 2004

In solving optimization problems for building design and control, the cost function is often eval... more In solving optimization problems for building design and control, the cost function is often evaluated using a detailed building simulation program. These programs contain code features that cause the cost function to be discontinuous. Optimization algorithms that require smoothness can fail on such problems. Evaluating the cost function is often so time-consuming that stochastic optimization algorithms are run using only a few simulations, which decreases the probability of getting close to a minimum. To show how applicable direct search, stochastic, and gradient-based optimization algorithms are for solving such optimization problems, we compare the performance of these algorithms in minimizing cost functions with di erent smoothness. We also explain what causes the large discontinuities in the cost functions.

Journal on artificial intelligence, 2022

The world has been suffering from the Coronavirus (COVID-19) pandemic since its appearance in lat... more The world has been suffering from the Coronavirus (COVID-19) pandemic since its appearance in late 2019. COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide. Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease. In this paper, we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases. Specifically, we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system. Particularly, lightweight EfficientNet networks were used to build classification models able to discriminate between positive and negative COVID-19 cases using chest X-ray images. The proposed models ensure a trade-off between scaling down the architecture of the deep network to reduce the computational cost and optimizing the classification performance. In the experiments, a public dataset containing 7,345 chest X-ray images was used to train, validate and test the proposed models for binary and multiclass classification problems, respectively. The obtained results showed the EfficientNet-elite-B9-V2, which is the lightest proposed model yielded an accuracy of 96%. On the other hand, EfficientNet-lite-B0 overtook the other models, and achieved an accuracy of 99%.

International Journal of Advanced Computer Science and Applications, 2021

Recycling plays a vital role in saving the planet for future generations as it allows keeping a c... more Recycling plays a vital role in saving the planet for future generations as it allows keeping a clean environment, reducing energy consumption, and saving materials. Of special interest is the plastic material which may take centuries to decompose. In particular, the Polyethylene Terephthalate (PET) is a widely used plastic for packaging various products that can be recycled. Sorting PET can be performed, either manually or automatically, at recycling facilities where the post-consumed objects are moving on the conveyor belt. In particular, automated sorting can process a large amount of PET objects without human intervention. In this paper, we propose a computer vision system for recognizing PET objects placed on a conveyor belt. Specifically, DeepLabv3+ is deployed to segment PET objects semantically. Such system can be exploited using an autonomous robot to compensate for human intervention and supervision. The conducted experiments showed that the proposed system outperforms the state of the art semantic segmentation approaches with weighted IoU equals to 97% and Mean BFscore equals to 89%.

Computer Science & Information Technology (CS & IT), Apr 29, 2017

Spectroscopy or hyperspectral imaging consists in the acquisition, analysis, and extraction of th... more Spectroscopy or hyperspectral imaging consists in the acquisition, analysis, and extraction of the spectral information measured on a specific region or object using an airborne or satellite device. Hyperspectral imaging has become an active field of research recently. One way of analysing such data is through clustering. However, due to the high dimensionality of the data and the small distance between the different material signatures, clustering such a data is a challenging task.In this paper, we empirically compared five clustering techniques in different hyperspectral data sets. The considered clustering techniques are K-means, K-medoids, fuzzy Cmeans, hierarchical, and density-based spatial clustering of applications with noise. Four data sets are used to achieve this purpose which is Botswana, Kennedy space centre, Pavia, and Pavia University. Beside the accuracy, we adopted four more similarity measures: Rand statistics, Jaccard coefficient, Fowlkes-Mallows index, and Hubert index. According to accuracy, we found that fuzzy C-means clustering is doing better on Botswana and Pavia data sets, K-means and K-medoids are giving better results on Kennedy space centre data set, and for Pavia University the hierarchical clustering is better.

International Journal of Advanced Computer Science and Applications

Deafness does not restrict its negative effect on the person's hearing, but rather on all aspect ... more Deafness does not restrict its negative effect on the person's hearing, but rather on all aspect of their daily life. Moreover, hearing people aggravated the issue through their reluctance to learn sign language. This resulted in a constant need for human translators to assist deaf person which represents a real obstacle for their social life. Therefore, automatic sign language translation emerged as an urgent need for the community. The availability and the widespread use of mobile phones equipped with digital cameras promoted the design of image-based Arabic Sign Language (ArSL) recognition systems. In this work, we introduce a new ArSL recognition system that is able to localize and recognize the alphabet of the Arabic sign language using a Faster Region-based Convolutional Neural Network (R-CNN). Specifically, faster R-CNN is designed to extract and map the image features, and learn the position of the hand in a given image. Additionally, the proposed approach alleviates both challenges; the choice of the relevant features used to encode the sign visual descriptors, and the segmentation task intended to determine the hand region. For the implementation and the assessment of the proposed Faster R-CNN based sign recognition system, we exploited VGG-16 and ResNet-18 models, and we collected a real ArSL image dataset. The proposed approach yielded 93% accuracy and confirmed the robustness of the proposed model against drastic background variations in the captured scenes.

All in-text references underlined in blue are linked to publications on ResearchGate, letting you... more All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.

International Journal of Fuzzy Logic and Intelligent Systems, 2013

For real-world clustering tasks, the input data is typically not easily separable due to the high... more For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.

AI Communications, 2015

We propose a framework for automatic verbal offense detection in social network comments. The pro... more We propose a framework for automatic verbal offense detection in social network comments. The proposed approach adapts a possibilistic based fusion method to different regions of the feature space in order to classify social network comments as insult or not. The proposed algorithm is formulated mathematically through the minimization of some objective functions. It combines context identification and multi-algorithm fusion criteria into a joint objective function. The optimization is intended to produce contexts as compact clusters in subspaces of the high-dimensional feature space via possibilistic unsupervised learning and feature discrimination. The clustering component associates a degree of typicality with each data sample in order to identify and reduce the influence of noise points and outliers. Also, the approach provides optimal fusion parameters for each context. Our initial experiments on synthetic datasets and standard SMS datasets indicate that the proposed fusion approach outperforms individual classifiers. Finally, the proposed system is assessed using real collection of social network comments, and compared to state-of-the-art fusion technique.

2012 19th IEEE International Conference on Image Processing, 2012

ABSTRACT A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple ... more ABSTRACT A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. This algorithm, the Piece-wise Convex Multiple Model Endmember Detection (P-COMMEND) algorithm, models a hyperspectral image using a piece-wise convex representation. By using a piece-wise convex representation, non-convex hyperspectral data are more accurately characterized. For example, the well-known Indian Pines hyperspectral image is used as an example of a piece-wise convex collection of pixels. The convex regions, weights, endmembers and abundances are found using an iterative fuzzy clustering method. Results indicate that the piece-wise convex representation provides endmembers that better represent hyperspectral data sets over methods that use a single convex region.

An endmember detection and spectral unmixing algorithm that uses both spatial and spectral inform... more An endmember detection and spectral unmixing algorithm that uses both spatial and spectral information is presented. This method, Spatial Piece-wise Convex Multiple Model Endmember Detection (Spatial P-COMMEND), autonomously estimates multiple sets of endmembers and performs spectral unmixing for input hyperspectral data. Spatial P-COMMEND does not restrict the estimated endmembers to define a single convex region during spectral unmixing. Instead, a piecewise convex representation is used that can effectively represent non-convex hyperspectral data. Spatial P-COMMEND drives neighboring pixels to be unmixed by the same set of endmembers encouraging spatially-smooth unmixing results.

Pattern Analysis and Applications, Mar 21, 2015

We propose a new relational clustering approach, called Fuzzy clustering with Learnable Cluster d... more We propose a new relational clustering approach, called Fuzzy clustering with Learnable Cluster dependent Kernels (FLeCK), that learns multiple kernels while seeking compact clusters. A Gaussian kernel is learned with respect to each cluster. It reflects the relative density, size, and position of the cluster with respect to the other clusters. These kernels are learned by optimizing both the intra-cluster and the intercluster similarities. Moreover, FLeCK is formulated to work on relational data. This makes it applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. The experiments show that FLeCK outperforms several other algorithms. In particular, we show that when data include clusters with various inter and intra cluster distances, learning cluster dependent kernel is crucial in obtaining a good partition.

International Journal of Advanced Computer Science and Applications, 2022

Communication is a critical skill for humans. People who have been deprived from communicating th... more Communication is a critical skill for humans. People who have been deprived from communicating through words like the rest of humans, usually use sign language. For sign language, the main signs features are the handshape, the location, the movement, the orientation and the non-manual component. The vast spread of mobile phones presents an opportunity for hearing-disabled people to engage more into their communities. Designing and implementing a novel Arabic Sign Language (ArSL) recognition system would significantly affect their quality of life. Deep learning models are usually heavy for mobile phones. The more layers a neural network has, the heavier it is. However, typical deep neural network necessitates a large number of layers to attain adequate classification performance. This project aims at addressing the Arabic Sign Language recognition problem and ensuring a trade-off between optimizing the classification performance and scaling down the architecture of the deep network to reduce the computational cost. Specifically, we adapted Efficient Network (EfficientNet) models and generated lightweight deep learning models to classify Arabic Sign Language gestures. Furthermore, a real dataset collected by many different signers to perform hand gestures for thirty different Arabic alphabets. Then, an appropriate performance metrics used in order to assess the classification outcomes obtained by the proposed lightweight models. Besides, preprocessing and data augmentation techniques were investigated to enhance the models generalization. The best results were obtained using the EfficientNet-Lite 0 architecture and the Label smooth as loss function. Our model achieved 94% and proved to be effective against background variations.

Applied sciences, Nov 18, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Applied Sciences

Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learn... more Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing semi-supervised clustering approaches are relatively scarce compared to the contributions made to enhance the state-of-the-art fully unsupervised clustering approaches. In this paper, we propose a novel semi-supervised deep clustering approach, named Soft Constrained Deep Clustering (SC-DEC), that aims to address the limitations exhibited by existing semi-supervised clustering approaches. Specifically, the proposed approach leverages a deep neural network architecture and generates fuzzy membership degrees that better reflect the true partition of the data. In particular, the proposed approach uses side-information and formulates it as a set of soft pairwise constraints to supervise the machine learning process. This supervision information is ex...

This paper reviews multiple query approaches for Content-Based Image Retrieval systems (MQIR). Th... more This paper reviews multiple query approaches for Content-Based Image Retrieval systems (MQIR). These are recently proposed Content-Based Image Retrieval systems that enhance the retrieval performance by conveying a richer understanding of the user high-level interest to the retrieval system. In fact, by allowing the user to express his interest using a set of query images, MQIR bridge the semantic gap with the low-level image features. Nevertheless, the main challenge of MQRI systems is how to compute the distances between the set of query images and each image in the database in a way that enhances the retrieval results and reflects the highlevel semantic the user is interested in. For this matter, several approaches have been reported in the literature. In this paper, we investigate existing multiple query retrieval systems. We describe each approach, detail the way it computes the distances between the set of query images and each image in the database, and analyze its advantages...

International Journal of Advanced Computer Science and Applications, 2021

Clustering is a fundamental problem in machine learning. To address this, a large number of algor... more Clustering is a fundamental problem in machine learning. To address this, a large number of algorithms have been developed. Some of these algorithms, such as K-means, handle the original data directly, while others, such as spectral clustering, apply linear transformation to the data. Still others, such as kernel-based algorithms, use nonlinear transformation. Since the performance of the clustering depends strongly on the quality of the data representation, representation learning approaches have been extensively researched. With the recent advances in deep learning, deep neural networks are being increasingly utilized to learn clustering-friendly representation. We provide here a review of existing algorithms that are being used to jointly optimize deep neural networks and clustering methods.

International Journal of Advanced Computer Science and Applications, 2020

In this paper, we propose a Computer Aided Diagnosis (CAD) system in order to assist the physicia... more In this paper, we propose a Computer Aided Diagnosis (CAD) system in order to assist the physicians in the early detection of Alzheimer's Disease (AD) and ensure an effective diagnosis. The proposed framework is designed to be fully-automated upon the capture of the brain structure using Magnetic Resonance Imaging (MRI) scanners. The Voxel-Based Morphometry (VBM) analysis is a key element in the proposed detection process as it is intended to investigate the Gray Matter (GM) tissues in the captured MRI images. In other words, the feature extraction phase consists in encoding the voxel properties in the MRI images into numerical vectors. The resulting feature vectors are then fed into a Neighborhood Component Analysis and Feature Selection (NCFS) algorithm coupled with K-Nearest Neighbor (KNN) algorithm in order to learn a classification model able to recognize AD cases. The feature selection based on NCFS algorithm improved the overall classification performance.

Journal of Computer Science, 2020

As the number of fall incidents among elderly people and patients are continuously growing, resea... more As the number of fall incidents among elderly people and patients are continuously growing, researches boosted their researches to propose efficient automatic fall detection systems. In particular, they formulated the fall detection problem as a supervised learning task where some visual features are extracted from the video frames and used to automatically identify the position of a human as "Fall" or "Non-Fall" based on a model learned using labeled training frames. Despite the promising reported results, existing fall detection systems exhibit noticeable room for improvement. Learner fusion which builds multiple models and aggregates their respective decisions is an alternative that would improve the fall detection performance. In this paper, an image-based fall detection system that captures the visual property and the spatial position of the human body using the Histogram of Oriented Gradient from the video frames is proposed. Then, the extracted features are used to train three classification models. Namely, the Naïve Bayes, the K-Nearest Neighbors and the Support Vector Machine algorithms are adopted. Next, the majority vote is used to aggregate the decisions of the individual learners. The proposed system was assessed using a standard dataset and yielded promising results. Standard performance measures along with the statistical significance t-test were used to prove that the fall detection system based on majority vote fusion outperforms the individual classifier based approaches.

Journal of Computer Science and Technology, 2017

Building and Environment, 2004

In solving optimization problems for building design and control, the cost function is often eval... more In solving optimization problems for building design and control, the cost function is often evaluated using a detailed building simulation program. These programs contain code features that cause the cost function to be discontinuous. Optimization algorithms that require smoothness can fail on such problems. Evaluating the cost function is often so time-consuming that stochastic optimization algorithms are run using only a few simulations, which decreases the probability of getting close to a minimum. To show how applicable direct search, stochastic, and gradient-based optimization algorithms are for solving such optimization problems, we compare the performance of these algorithms in minimizing cost functions with di erent smoothness. We also explain what causes the large discontinuities in the cost functions.

Journal on artificial intelligence, 2022

The world has been suffering from the Coronavirus (COVID-19) pandemic since its appearance in lat... more The world has been suffering from the Coronavirus (COVID-19) pandemic since its appearance in late 2019. COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide. Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease. In this paper, we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases. Specifically, we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system. Particularly, lightweight EfficientNet networks were used to build classification models able to discriminate between positive and negative COVID-19 cases using chest X-ray images. The proposed models ensure a trade-off between scaling down the architecture of the deep network to reduce the computational cost and optimizing the classification performance. In the experiments, a public dataset containing 7,345 chest X-ray images was used to train, validate and test the proposed models for binary and multiclass classification problems, respectively. The obtained results showed the EfficientNet-elite-B9-V2, which is the lightest proposed model yielded an accuracy of 96%. On the other hand, EfficientNet-lite-B0 overtook the other models, and achieved an accuracy of 99%.

International Journal of Advanced Computer Science and Applications, 2021

Recycling plays a vital role in saving the planet for future generations as it allows keeping a c... more Recycling plays a vital role in saving the planet for future generations as it allows keeping a clean environment, reducing energy consumption, and saving materials. Of special interest is the plastic material which may take centuries to decompose. In particular, the Polyethylene Terephthalate (PET) is a widely used plastic for packaging various products that can be recycled. Sorting PET can be performed, either manually or automatically, at recycling facilities where the post-consumed objects are moving on the conveyor belt. In particular, automated sorting can process a large amount of PET objects without human intervention. In this paper, we propose a computer vision system for recognizing PET objects placed on a conveyor belt. Specifically, DeepLabv3+ is deployed to segment PET objects semantically. Such system can be exploited using an autonomous robot to compensate for human intervention and supervision. The conducted experiments showed that the proposed system outperforms the state of the art semantic segmentation approaches with weighted IoU equals to 97% and Mean BFscore equals to 89%.

Computer Science & Information Technology (CS & IT), Apr 29, 2017

Spectroscopy or hyperspectral imaging consists in the acquisition, analysis, and extraction of th... more Spectroscopy or hyperspectral imaging consists in the acquisition, analysis, and extraction of the spectral information measured on a specific region or object using an airborne or satellite device. Hyperspectral imaging has become an active field of research recently. One way of analysing such data is through clustering. However, due to the high dimensionality of the data and the small distance between the different material signatures, clustering such a data is a challenging task.In this paper, we empirically compared five clustering techniques in different hyperspectral data sets. The considered clustering techniques are K-means, K-medoids, fuzzy Cmeans, hierarchical, and density-based spatial clustering of applications with noise. Four data sets are used to achieve this purpose which is Botswana, Kennedy space centre, Pavia, and Pavia University. Beside the accuracy, we adopted four more similarity measures: Rand statistics, Jaccard coefficient, Fowlkes-Mallows index, and Hubert index. According to accuracy, we found that fuzzy C-means clustering is doing better on Botswana and Pavia data sets, K-means and K-medoids are giving better results on Kennedy space centre data set, and for Pavia University the hierarchical clustering is better.

International Journal of Advanced Computer Science and Applications

Deafness does not restrict its negative effect on the person's hearing, but rather on all aspect ... more Deafness does not restrict its negative effect on the person's hearing, but rather on all aspect of their daily life. Moreover, hearing people aggravated the issue through their reluctance to learn sign language. This resulted in a constant need for human translators to assist deaf person which represents a real obstacle for their social life. Therefore, automatic sign language translation emerged as an urgent need for the community. The availability and the widespread use of mobile phones equipped with digital cameras promoted the design of image-based Arabic Sign Language (ArSL) recognition systems. In this work, we introduce a new ArSL recognition system that is able to localize and recognize the alphabet of the Arabic sign language using a Faster Region-based Convolutional Neural Network (R-CNN). Specifically, faster R-CNN is designed to extract and map the image features, and learn the position of the hand in a given image. Additionally, the proposed approach alleviates both challenges; the choice of the relevant features used to encode the sign visual descriptors, and the segmentation task intended to determine the hand region. For the implementation and the assessment of the proposed Faster R-CNN based sign recognition system, we exploited VGG-16 and ResNet-18 models, and we collected a real ArSL image dataset. The proposed approach yielded 93% accuracy and confirmed the robustness of the proposed model against drastic background variations in the captured scenes.

All in-text references underlined in blue are linked to publications on ResearchGate, letting you... more All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.

International Journal of Fuzzy Logic and Intelligent Systems, 2013

For real-world clustering tasks, the input data is typically not easily separable due to the high... more For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.

AI Communications, 2015

We propose a framework for automatic verbal offense detection in social network comments. The pro... more We propose a framework for automatic verbal offense detection in social network comments. The proposed approach adapts a possibilistic based fusion method to different regions of the feature space in order to classify social network comments as insult or not. The proposed algorithm is formulated mathematically through the minimization of some objective functions. It combines context identification and multi-algorithm fusion criteria into a joint objective function. The optimization is intended to produce contexts as compact clusters in subspaces of the high-dimensional feature space via possibilistic unsupervised learning and feature discrimination. The clustering component associates a degree of typicality with each data sample in order to identify and reduce the influence of noise points and outliers. Also, the approach provides optimal fusion parameters for each context. Our initial experiments on synthetic datasets and standard SMS datasets indicate that the proposed fusion approach outperforms individual classifiers. Finally, the proposed system is assessed using real collection of social network comments, and compared to state-of-the-art fusion technique.

2012 19th IEEE International Conference on Image Processing, 2012

ABSTRACT A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple ... more ABSTRACT A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. This algorithm, the Piece-wise Convex Multiple Model Endmember Detection (P-COMMEND) algorithm, models a hyperspectral image using a piece-wise convex representation. By using a piece-wise convex representation, non-convex hyperspectral data are more accurately characterized. For example, the well-known Indian Pines hyperspectral image is used as an example of a piece-wise convex collection of pixels. The convex regions, weights, endmembers and abundances are found using an iterative fuzzy clustering method. Results indicate that the piece-wise convex representation provides endmembers that better represent hyperspectral data sets over methods that use a single convex region.

An endmember detection and spectral unmixing algorithm that uses both spatial and spectral inform... more An endmember detection and spectral unmixing algorithm that uses both spatial and spectral information is presented. This method, Spatial Piece-wise Convex Multiple Model Endmember Detection (Spatial P-COMMEND), autonomously estimates multiple sets of endmembers and performs spectral unmixing for input hyperspectral data. Spatial P-COMMEND does not restrict the estimated endmembers to define a single convex region during spectral unmixing. Instead, a piecewise convex representation is used that can effectively represent non-convex hyperspectral data. Spatial P-COMMEND drives neighboring pixels to be unmixed by the same set of endmembers encouraging spatially-smooth unmixing results.