Multimedia processing using deep learning technologies, high‐performance computing cloud resources, and Big Data volumes. (21st February 2020)

• Record Type: Journal Article
• Authors: Mahmoudi, Sidi Ahmed; Belarbi, Mohammed Amin; Mahmoudi, Saïd; Belalem, Ghalem; Manneback, Pierre
• Other Names: Jeon Gwanggil guestEditor.; Bellandi Valerio guestEditor.; Bakhouya Mohamed guestEditor.; Zbakh Mostapha guestEditor.; Essaaidi Mohamed guestEditor.; Manneback Pierre guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 17(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-21
• Subjects: Big Data -- cloud computing -- deep Learning -- high performance computing -- images indexation and retrieval -- multimedia processing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5699
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The last few years have been marked by the presence of very large sets of images and videos in our everyday lives. These multimedia objects have a very fast frequency of creation and sharing since images and videos can come from different devices such as smartphones, satellites, cameras, or drones. They are generally used to illustrate objects in different situations (public areas, train stations, hospitals, political and sport events and competitions, etc). As consequence, image and video processing algorithms have got increasing importance for several computer vision applications that should be adapted for managing large‐scale volumes and exploiting high performance computing resources (local or cloud). In this work, we propose a cloud‐based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can (i) exploit high performance computing cloud resources, (ii) execute applications in real time, and (iii) manage large‐scale database using Big Data technologies. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different applications without the need to download, install, and configure software or hardware. Experiments were conducted using three kinds of applications: (i) image and video processing applications, (ii) deep learning techniques for images classification and multiobject localization, and (iii) images indexation and retrieval. These experiments demonstrated the interest of our platform for sharing, in an efficient way, our scientific contributions and annotated databases in order to improve the quality and performance of computer vision applications.