Iterative Random Visual Word Selection
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
DOI :
Title :
Iterative Random Visual Word Selection
Author(s) :
Urruty, Thierry [Auteur]
Synthèse et analyse d'images [XLIM-ASALI]
Gbehounou, Syntyche [Auteur]
Synthèse et analyse d'images [XLIM-ASALI]
Le, Huu Ton [Auteur]
Synthèse et analyse d'images [XLIM-ASALI]
Martinet, Jean [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Fernandez-Maloigne, Christine [Auteur]
Synthèse et analyse d'images [XLIM-ASALI]
Synthèse et analyse d'images [XLIM-ASALI]
Gbehounou, Syntyche [Auteur]
Synthèse et analyse d'images [XLIM-ASALI]
Le, Huu Ton [Auteur]
Synthèse et analyse d'images [XLIM-ASALI]
Martinet, Jean [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Fernandez-Maloigne, Christine [Auteur]
Synthèse et analyse d'images [XLIM-ASALI]
Conference title :
ICMR 2014 ACM International Conference on Multimedia Retrieval
City :
Glasgow
Country :
Royaume-Uni
Start date of the conference :
2014-04-01
HAL domain(s) :
Informatique [cs]/Recherche d'information [cs.IR]
English abstract : [en]
In content based image retrieval, one of the most important step is the construction of image signatures. To do so, a part of state-of-the-art approaches propose to build a visual vocabulary. In this paper, we propose a ...
Show more >In content based image retrieval, one of the most important step is the construction of image signatures. To do so, a part of state-of-the-art approaches propose to build a visual vocabulary. In this paper, we propose a new methodology for visual vocabulary construction that obtains high retrieval results. Moreover, it is computationally inexpensive to build and needs no prior knowledge on features or dataset used.Classically, the vocabulary is built by aggregating a certain number of features in centroids using a clustering algorithm. The final centroids are assimilated to visual "words". Our approach for building a visual vocabulary is based on an iterative random visual word selection mixing a saliency map and tf-idf scheme. Experiment results show that it outperforms the original "Bag of visual words" based approach in efficiency and effectiveness.Show less >
Show more >In content based image retrieval, one of the most important step is the construction of image signatures. To do so, a part of state-of-the-art approaches propose to build a visual vocabulary. In this paper, we propose a new methodology for visual vocabulary construction that obtains high retrieval results. Moreover, it is computationally inexpensive to build and needs no prior knowledge on features or dataset used.Classically, the vocabulary is built by aggregating a certain number of features in centroids using a clustering algorithm. The final centroids are assimilated to visual "words". Our approach for building a visual vocabulary is based on an iterative random visual word selection mixing a saliency map and tf-idf scheme. Experiment results show that it outperforms the original "Bag of visual words" based approach in efficiency and effectiveness.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
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