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Karamti H.,University of Sfax | Tmar M.,Institute Superieur Dinformatique Et Of Multimedia Of Sfax | Visani M.,University of La Rochelle | Visani M.,University of Science and Technology Houari Boumediene | And 2 more authors.
Multimedia Tools and Applications | Year: 2017

Image retrieval is an important problem for researchers in computer vision and content-based image retrieval (CBIR) fields. Over the last decades, many image retrieval systems were based on image representation as a set of extracted low-level features such as color, texture and shape. Then, systems calculate similarity metrics between features in order to find similar images to a query image. The disadvantage of this approach is that images visually and semantically different may be similar in the low level feature space. So, it is necessary to develop tools to optimize retrieval of information. Integration of vector space models is one solution to improve the performance of image retrieval. In this paper, we present an efficient and effective retrieval framework which includes a vectorization technique combined with a pseudo relevance model. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Wang, Oxford5k and Inria Holidays datasets to show the performance of our proposed framework. © 2017 Springer Science+Business Media New York

Eddahech A.,University of Sfax | Chtourou S.,Institute Superieur Dinformatique Et Of Multimedia Of Sfax | Chtourou M.,University of Sfax
Journal of Systems Architecture | Year: 2013

Multimedia design such as video decoders are typically composed of several communicating tasks. Each task is characterized by its workload variation. The target device of this kind of application contains several processing unit. This calls for a dynamic management of hardware units to improve the QOS of the application and to optimally allocate resources. In this paper, we propose a new architecture based on hierarchical multilevel neural network to model workload variation of each task. The hierarchical structure of this neural network perfectly describes the multilevel decomposition of each hardware unit. The aim of this investigation is to build a design with a control unit that manages the architecture and resource allocation according to the neural network workload prediction. © 2012 Elsevier B.V. All rights reserv.

Jaziri W.,Institute Superieur Dinformatique Et Of Multimedia Of Sfax | Sassi N.,Institute Superieur Dinformatique Et Of Multimedia Of Sfax | Gargouri F.,Institute Superieur Dinformatique Et Of Multimedia Of Sfax | Miracl L.,Institute Superieur Dinformatique Et Of Multimedia Of Sfax
International Journal of Metadata, Semantics and Ontologies | Year: 2010

The concept of ontology is more and more used to provide a shared understanding of a domain of interest and to enhance communication among humans, computers and software. However, ontologies are often used in changing environments and, therefore, must be adapted to evolution requirements. This paper proposes an approach to manage the ontology evolution and to maintain its coherence after changing. This approach anticipates incoherencies that can be generated and proposes additional operations to correct them. To assist users in expressing and applying evolution requirements, an ontology evolution tool has been developed and applied to develop the Tunisian Education system. Copyright © 2010 Inderscience Enterprises Ltd.

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