Ranking user-annotated images for multiple query terms - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2009

Ranking user-annotated images for multiple query terms

Moray Allan
  • Fonction : Auteur
  • PersonId : 865279
Jakob Verbeek

Résumé

We show how web image search can be improved by taking into account the users who provided different images, and that performance when searching for multiple terms can be increased by learning a new combined model and taking account of images which partially match the query. Search queries are answered by using a mixture of kernel density estimators to rank the visual content of web images from the Flickr website whose noisy tag annotations match the given query terms. Experiments show that requiring agreement between images from different users allows a better model of the visual class to be learnt, and that precision can be increased by rejecting images from 'untrustworthy' users. We focus on search queries for multiple terms, and demonstrate enhanced performance by learning a single model for the overall query, treating images which only satisfy a subset of the search terms as negative training examples.
Fichier principal
Vignette du fichier
verbeek09bmvc.pdf (27.4 Mo) Télécharger le fichier
Vignette du fichier
AV09.png (541.58 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Format : Figure, Image
Loading...

Dates et versions

inria-00439278 , version 1 (25-01-2011)
inria-00439278 , version 2 (11-04-2011)

Identifiants

Citer

Moray Allan, Jakob Verbeek. Ranking user-annotated images for multiple query terms. BMVC 2009 - British Machine Vision Conference, Sep 2009, London, United Kingdom. pp.20.1-20.10, ⟨10.5244/C.23.20⟩. ⟨inria-00439278v2⟩
336 Consultations
665 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More