Visual Features with Semantic Combination Using Bayesian Network for a More Effective Image Retrieval

Sabine Barrat 1 Salvatore Tabbone 1
1 QGAR - Querying Graphics through Analysis and Recognition
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In many vision problems, instead of having fully annotated training data, it is easier to obtain just a subset of data with annotations, because it is less restrictive for the user. For this reason, in this paper, we consider especially the problem of weakly-annotated image retrieval, where just a small subset of the database is annotated with keywords. We present and evaluate a new method which improves the effectiveness of content-based image retrieval, by integrating semantic concepts extracted from text. Our model is inspired from the probabilistic graphical model theory: we propose a hierarchical mixture model which enables to handle missing values and to capture the user's preference by also considering a relevance feedback process. Results of visual-textual retrieval associated to a relevance feedback process, reported on a database of images collected from the Web, partially and manually annotated, show an improvement of about 44.5% in terms of recognition rate against content-based retrieval.
Type de document :
Communication dans un congrès
ICPR, Dec 2008, Tampa, United States. 2008
Liste complète des métadonnées

Littérature citée [6 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00339114
Contributeur : Sabine Barrat <>
Soumis le : dimanche 16 novembre 2008 - 21:24:20
Dernière modification le : jeudi 11 janvier 2018 - 06:19:59
Document(s) archivé(s) le : lundi 7 juin 2010 - 21:31:38

Fichier

icpr08.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00339114, version 1

Collections

Citation

Sabine Barrat, Salvatore Tabbone. Visual Features with Semantic Combination Using Bayesian Network for a More Effective Image Retrieval. ICPR, Dec 2008, Tampa, United States. 2008. 〈inria-00339114〉

Partager

Métriques

Consultations de la notice

143

Téléchargements de fichiers

73