Deep Learning content-based search in media files (images and videos) / Ihssane Khadidja Bendjazia

Public ISBD Titre : Deep Learning content-based search in media files (images and videos) Type de document : document multimédia Auteurs : Ihssane Khadidja Bendjazia, Auteur ; Leila Benarous, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2024 Importance : 52 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Distributed networks, systems, and applications Langues : Anglais Mots-clés : Object Detection  Search  Image  Video  AI  YOLOv8  multimedia Résumé : The rapid growth of digital multimedia content has created a new demand for technologies that not only process but also sort and locate images and videos in the oceans of content. Generally, text-based traditional search engines are sometimes not able to retrieve specific visual content based on visual features, leading to technology development in computer vision for content-based image and video retrieval. This work presents the design, implementation, and evaluation of a user-friendly application, "Search by meaning and visual clues," that utilizes AI and YOLOv8 to offer users content-based search features. Our application allows users to probe multimedia sources using the image or video as input, which removes the laborious text-based queries. By employing modern computer vision, our application aims to produce prompt accurate search results with user friendliness being a top priority. note de thèses : Mémoire de master en informatique Deep Learning content-based search in media files (images and videos) [document multimédia] / Ihssane Khadidja Bendjazia, Auteur ; Leila Benarous, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2024 . - 52 p. + 1 disque optique numérique (CD-ROM). Option : Distributed networks, systems, and applications Langues : Anglais Mots-clés : Object Detection  Search  Image  Video  AI  YOLOv8  multimedia Résumé : The rapid growth of digital multimedia content has created a new demand for technologies that not only process but also sort and locate images and videos in the oceans of content. Generally, text-based traditional search engines are sometimes not able to retrieve specific visual content based on visual features, leading to technology development in computer vision for content-based image and video retrieval. This work presents the design, implementation, and evaluation of a user-friendly application, "Search by meaning and visual clues," that utilizes AI and YOLOv8 to offer users content-based search features. Our application allows users to probe multimedia sources using the image or video as input, which removes the laborious text-based queries. By employing modern computer vision, our application aims to produce prompt accurate search results with user friendliness being a top priority. note de thèses : Mémoire de master en informatique

Réservation

Réserver ce document

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MF 01-79	MF 01-79	CD	BIBLIOTHEQUE DE FACULTE DES SCIENCES	théses (sci)	Disponible

---

page 1/1