Paper Info
Title
Utilisation de concepts visuels et de la diversité visuelle pour améliorer la recherche d'images
Abstract
In this article, we study (i) how to automatically extract an d exploit visual concepts and (ii) fast visual diversity. First, in the Visual ConceptDetection Task (VCDT), we look at the mutual exclusion and implication relations between VCD T concepts in order to improve the automatic image annotation by Forest of Fuzzy Decision T rees (FFDTs). Second, in the ImageCLEFphoto task, we use the FFDTs learnt in VCDT task and WordNet to improve image retrieval. Third, we apply a fast visual diversity method ba sed on space clustering to improve the cluster recall score. This study shows that there is a cle ar improvement, in terms of precision or cluster recall at 20, when using the visual concepts expli citly appearing in the query and that space clustering can be efficiently used to improve cluster r ecall.
Year
Venue
Keywords
2009
CORIA
arbre de déci- sions flous,visual concepts detection,détection de concept s visuels,diversification keywords:text-based images retrieval,f uzzy decision trees,mots-clés :recherche d'images basée sur le texte,automatic image annotation,image retrieval,mutual exclusion,decision tree
Field
DocType
Citations 
Automatic image annotation,Computer science,Image retrieval,Exploit,Artificial intelligence,Natural language processing,Cluster analysis,WordNet,Recall,Mutual exclusion,Machine learning,Fuzzy decision
Conference
0
PageRank 
References 
Authors
0.34
8
6
Name
Order
Citations
PageRank
Sabrina Tollari19113.64
Marcin Detyniecki233039.95
Ali Fakeri-Tabrizi3376.07
Christophe Marsala423734.77
Massih-reza Amini549748.05
Patrick Gallinari61856187.19