Region-Based Image Retrieval Using Relevance Feature Weights

Bchir, Ouiem,Ben Ismail, Mohamed Maher,Aljam, Hadeel Korean Institute of Intelligent Systems 2018 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.18 No.1

We propose a new region-based CBIR (content-based image retrieval) system. One of the main objectives of our work is to reduce the semantic gap between the visual characteristics of the query and the high level semantic sought by the user. This is achieved by allowing the user to select specific regions and expressing his interest in a more accurate way. Moreover, the proposed approach overcomes the challenge of choosing suitable features to describe the image content. More specifically, relevance weights are automatically associated with each visual feature in order to better represent the visual content of the images. To evaluate these objectives, we compare the obtained results with those obtained using traditional CBIR systems.

Region-Based Image Retrieval Using Relevance Feature Weights

Ouiem Bchir,Mohamed Maher Ben Ismail,Hadeel Aljam 한국지능시스템학회 2018 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.18 No.1

We propose a new region-based CBIR (content-based image retrieval) system. One of the main objectives of our work is to reduce the semantic gap between the visual characteristics of the query and the high level semantic sought by the user. This is achieved by allowing the user to select specific regions and expressing his interest in a more accurate way. Moreover, the proposed approach overcomes the challenge of choosing suitable features to describe the image content. More specifically, relevance weights are automatically associated with each visual feature in order to better represent the visual content of the images. To evaluate these objectives, we compare the obtained results with those obtained using traditional CBIR systems.

The Cost of Non-maghreb Achieving the Gains from Economic Integration

( Mohammed Hedi Bchir ),( Hakim Ben Hammouda ),( Nassim Oulmane ),( Mustapha Sadni Jallab ) 세종대학교 경제통합연구소 (구 세종대학교 국제경제연구소) 2007 Journal of Economic Integration Vol.22 No.3

The purpose of this paper is to estimate the impact of the integration of Maghreb countries into a free trade area on the main macroeconomic aggregates. By using the MIRAGE model and MacMap database, we tested different scenarios to estimate the gains or the potential losses of various plans of trade integration (Free trade area for the Maghreb countries, Custom Union between Maghreb countries, Maghreban Common Market). Our study suggests that the overall gains from liberalizing trade in goods (and removing various regulatory non-tariff barriers in the process) could reach at least USD 350 million. The increase in revenue through increases in production and wages would positively affect welfare levels for Maghreb consumers. The dynamic gains from liberalizing trade in goods can outstrip the static gains, with productivity improvements as the main driver. Our analysis shows that the creation of a common market is probably the most interesting and efficient option for the Maghreb countries.

The Necessity to Balance the Barcelona Process: Economic Integration between North Africa and the European Union

( Mohamed Hedi Bchir ),( Hakim Ben Hammouda ),( Mondher Mimouni ),( Xavier Pichot ) 세종대학교 경제통합연구소(구 세종대학교 국제경제연구소) 2011 Journal of Economic Integration Vol.26 No.2

The Barcelona process is an ongoing process and its first economic results are still insignificant. This study had two objectives. The first is to provide a global assessment of its impacts on the three North African countries that have already became members of this process, namely Tunisia, Morocco and Egypt. The second objective is to explore its potentials by a larger inclusion of agriculture in the Euromed partnership. The approach adopted in this paper is the use of the MIRAGE global Computable General equilibrium Model and the MacMAPS database that gives a very detailed picture of the bilateral protection between all the WTO members. This approach allowed us to consider all the economic interactions that exist between the North African countries and the European Union as well as the economic interaction between the North African countries themselves. In addition a special emphasis was given to the compute methods used to aggregate tariffs. Indeed, existing methods of aggregation are underestimating the level of tariffs applied by the European Union on agricultural products. This underestimation induces an underestimation of the effects of agriculture liberalization on North African economies. In this paper, we develop an original method of aggregation that attempts to deal with this problem and that gives a more accurate approximation of the market access barriers applied by the European countries. Our main contribution in this article is to develop an original approach of aggregation that aims to obtain a more realistic estimation of the tariffs faced by developing countries and by consequence to obtain a more realistic estimation of the economic impacts of agricultural liberalization between developed and developing countries.

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

Frigui, Hichem,Bchir, Ouiem,Baili, Naouel Korean Institute of Intelligent Systems 2013 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.13 No.4

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.

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

Hichem Frigui,Ouiem Bchir,Naouel Baili 한국지능시스템학회 2013 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.13 No.4

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.

Automatic Image Annotation using Possibilistic Clustering Algorithm

Mohamed Maher Ben Ismail,Sara N. Alfaraj,Ouiem Bchir 한국지능시스템학회 2019 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.19 No.4

In this paper, the proposed PCMRM (possibilistic based cross-media relevance model) annotates images based on their visual contents. PCMRM framework relies on unsupervised learning to group the visually similar image regions into homogeneous clusters, along with the cross-media relevance model (CMRM) that is used to estimate the joint distribution of textual keywords and images. Besides, the unsupervised learning task exploits the robustness to noise of a possibilistic clustering algorithm, and generates membership degrees that represent the typicality of image regions with respect to the obtained clusters. To validate and assess the proposed system, we used the standard Corel dataset. PCMRM produced promising results. The reported performance measures proved that the proposed automatic image annotation approach outperforms similar state of the art solutions. This attainment is mainly attributed to the exploitation of the possibilistic membership produced by the clustering algorithm which allowed accurate learning of the association between annotating labels and the visual content of the image regions.

Automatic Determination of the Number of Clusters for Semi-Supervised Relational Fuzzy Clustering

Norah Ibrahim Fantoukh,Mohamed Maher Ben Ismail,Ouiem Bchir 한국지능시스템학회 2020 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.20 No.2

Semi-supervised clustering relies on both labeled and unlabeled data to steer the clustering process towards optimal categorization and escape from local minima. In this paper, we propose a novel fuzzy relational semi-supervised clustering algorithm based on an adaptive local distance measure (SSRF-CA). The proposed clustering algorithm utilizes side-information and formulates it as a set of constraints to supervise the learning task. These constraints are expressed using reward and penalty terms, which are integrated into a novel objective function. In particular, we formulate the clustering task as an optimization problem through the minimization of the proposed objective function. Solving this optimization problem provides the optimal values of different objective function parameters and yields the proposed semi-supervised clustering algorithm. Along with its ability to perform data clustering and learn the underlying dissimilarity measure between the data instances, our algorithm determines the optimal number of clusters in an unsupervised manner. Moreover, the proposed SSRF-CA is designed to handle relational data. This makes it appropriate for applications where only pairwise similarity (or dissimilarity) information between data instances is available. In this paper, we proved the ability of the proposed algorithm to learn the appropriate local distance measures and the optimal number of clusters while partitioning the data using various synthetic and real-world benchmark datasets that contain varying numbers of clusters with diverse shapes. The experimental results revealed that the proposed SSRF-CA accomplished the best performance among other state-of-the-art algorithms and confirmed the outperformance of our clustering approach.