Applying Segmented Images by Louvain Method into Content-Based Image Retrieval - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Applying Segmented Images by Louvain Method into Content-Based Image Retrieval

Résumé

The amount of multimedia data has increased on personal computers and the Internet requires the essential to finding a particular image or a collection of images have enhanced of demands. It urges researchers to propose new sophisticated methods to retrieve the information one desires. In the case of, the legacy approach cannot grow up with the rapid rate of available data anymore. Therefore, content-based image retrieval (CBIR) has attracted many researchers to various fields. Content-based image retrieval models attempt to effort to automate data analysis and indexing. In this paper, we propose a content-based image retrieval system for real images. This method is using segmented images by the Louvain method [26] to create features in order to apply to the CBIR system based on the Bag-of-Visual-Words (BoVW) model. In order to evaluate the proposed method, we selected the Corel dataset which is composed of 10 classes [14] total of 1000 images in the dataset for the experiment. The experimental results are shown using qualitative and quantitative evaluations.
Fichier principal
Vignette du fichier
ICCASA 2021 - Applying Segmented Images by Louvain Method.pdf (3.77 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04089326 , version 1 (04-05-2023)

Identifiants

Citer

Tuyet-Ngan Vo, Mickael Coustaty, Jean-Loup Guillaume, Thanh-Khoa Nguyen, de Cao Tran. Applying Segmented Images by Louvain Method into Content-Based Image Retrieval. International Conference on Context-Aware Systems and Applications, Oct 2021, Ho Chi Minh City, Vietnam. pp.77-90, ⟨10.1007/978-3-030-93179-7_7⟩. ⟨hal-04089326⟩

Collections

L3I UNIV-ROCHELLE
5 Consultations
98 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More