InforLorV4, PascalFrancis, Corpus, bibRecord, 000117

Hypergraph-based image retrieval for graph-based representation

Identifieur interne : 000117 ( PascalFrancis/Corpus ); précédent : 000116; suivant : 000118

Hypergraph-based image retrieval for graph-based representation

Auteurs : Salim Jouili ; Salvatore Tabbone

Source :

Pattern recognition [ 0031-3203 ] ; 2012.

RBID : Pascal:12-0273033

Descripteurs français

Pascal (Inist)
- Hypergraphe, Recherche image, Théorie graphe, Indexation, Graphe conceptuel, Algorithme, Base de données, Recherche par contenu.

English descriptors

KwdEn :
- Algorithm, Conceptual graph, Content based retrieval, Database, Graph theory, Hypergraph, Image retrieval, Indexing.

Abstract

In this paper, we introduce a novel method for graph indexing. We propose a hypergraph-based model for graph data sets by allowing cluster overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we index the graph set by the hyperedge centroids. This model is interesting to traverse the data set and efficient to retrieve graphs.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A02	`01`			`@0 PTNRA8`
A03		`1`		`@0 Pattern recogn.`
A05				`@2 45`
A06				`@2 11`
A08	`01`	`1`	`ENG`	`@1 Hypergraph-based image retrieval for graph-based representation`
A11	`01`	`1`		`@1 JOUILI (Salim)`
A11	`02`	`1`		`@1 TABBONE (Salvatore)`
A14	`01`			`@1 EURA NOVA, 4 Rue Emile Francqui @2 1435 Mont-St-Guibert @3 BEL @Z 1 aut.`
A14	`02`			`@1 Universite de Lorraine-LORIA UMR 7503, BP 239 @2 54506 Vandoeuvre-lès-Nancy @3 FRA @Z 2 aut.`
A20				`@1 4054-4068`
A21				`@1 2012`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 15220 @5 354000507994780160`
A44				`@0 0000 @1 © 2012 INIST-CNRS. All rights reserved.`
A45				`@0 41 ref.`
A47	`01`	`1`		`@0 12-0273033`
A60				`@1 P`
A61				`@0 A`
A64	`01`	`1`		`@0 Pattern recognition`
A66	`01`			`@0 GBR`
C01	`01`		`ENG`	@0 In this paper, we introduce a novel method for graph indexing. We propose a hypergraph-based model for graph data sets by allowing cluster overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we index the graph set by the hyperedge centroids. This model is interesting to traverse the data set and efficient to retrieve graphs.
C02	`01`	`X`		`@0 001D04A05C`
C02	`02`	`X`		`@0 001D04A03`
C03	`01`	`X`	`FRE`	`@0 Hypergraphe @5 01`
C03	`01`	`X`	`ENG`	`@0 Hypergraph @5 01`
C03	`01`	`X`	`SPA`	`@0 Hipergráfico @5 01`
C03	`02`	`3`	`FRE`	`@0 Recherche image @5 02`
C03	`02`	`3`	`ENG`	`@0 Image retrieval @5 02`
C03	`03`	`X`	`FRE`	`@0 Théorie graphe @5 03`
C03	`03`	`X`	`ENG`	`@0 Graph theory @5 03`
C03	`03`	`X`	`SPA`	`@0 Teoría grafo @5 03`
C03	`04`	`X`	`FRE`	`@0 Indexation @5 04`
C03	`04`	`X`	`ENG`	`@0 Indexing @5 04`
C03	`04`	`X`	`SPA`	`@0 Indización @5 04`
C03	`05`	`X`	`FRE`	`@0 Graphe conceptuel @5 05`
C03	`05`	`X`	`ENG`	`@0 Conceptual graph @5 05`
C03	`05`	`X`	`SPA`	`@0 Grafo conceptual @5 05`
C03	`06`	`X`	`FRE`	`@0 Algorithme @5 06`
C03	`06`	`X`	`ENG`	`@0 Algorithm @5 06`
C03	`06`	`X`	`SPA`	`@0 Algoritmo @5 06`
C03	`07`	`X`	`FRE`	`@0 Base de données @5 07`
C03	`07`	`X`	`ENG`	`@0 Database @5 07`
C03	`07`	`X`	`SPA`	`@0 Base dato @5 07`
C03	`08`	`X`	`FRE`	`@0 Recherche par contenu @5 08`
C03	`08`	`X`	`ENG`	`@0 Content based retrieval @5 08`
C03	`08`	`X`	`SPA`	`@0 Búsqueda por contenido @5 08`
C07	`01`	`X`	`FRE`	`@0 Recherche information @5 09`
C07	`01`	`X`	`ENG`	`@0 Information retrieval @5 09`
C07	`01`	`X`	`SPA`	`@0 Búsqueda información @5 09`
N21				`@1 205`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

Format Inist (serveur)

NO :	PASCAL 12-0273033 INIST
ET :	Hypergraph-based image retrieval for graph-based representation
AU :	JOUILI (Salim); TABBONE (Salvatore)
AF :	EURA NOVA, 4 Rue Emile Francqui/1435 Mont-St-Guibert/Belgique (1 aut.); Universite de Lorraine-LORIA UMR 7503, BP 239/54506 Vandoeuvre-lès-Nancy/France (2 aut.)
DT :	Publication en série; Niveau analytique
SO :	Pattern recognition; ISSN 0031-3203; Coden PTNRA8; Royaume-Uni; Da. 2012; Vol. 45; No. 11; Pp. 4054-4068; Bibl. 41 ref.
LA :	Anglais
EA :	In this paper, we introduce a novel method for graph indexing. We propose a hypergraph-based model for graph data sets by allowing cluster overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we index the graph set by the hyperedge centroids. This model is interesting to traverse the data set and efficient to retrieve graphs.
CC :	001D04A05C; 001D04A03
FD :	Hypergraphe; Recherche image; Théorie graphe; Indexation; Graphe conceptuel; Algorithme; Base de données; Recherche par contenu
FG :	Recherche information
ED :	Hypergraph; Image retrieval; Graph theory; Indexing; Conceptual graph; Algorithm; Database; Content based retrieval
EG :	Information retrieval
SD :	Hipergráfico; Teoría grafo; Indización; Grafo conceptual; Algoritmo; Base dato; Búsqueda por contenido
LO :	INIST-15220.354000507994780160
ID :	12-0273033

Links to Exploration step

Pascal:12-0273033

Le document en format XML

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<ET>Hypergraph-based image retrieval for graph-based representation</ET>
<AU>JOUILI (Salim); TABBONE (Salvatore)</AU>
<AF>EURA NOVA, 4 Rue Emile Francqui/1435 Mont-St-Guibert/Belgique (1 aut.); Universite de Lorraine-LORIA UMR 7503, BP 239/54506 Vandoeuvre-lès-Nancy/France (2 aut.)</AF>
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<EA>In this paper, we introduce a novel method for graph indexing. We propose a hypergraph-based model for graph data sets by allowing cluster overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we index the graph set by the hyperedge centroids. This model is interesting to traverse the data set and efficient to retrieve graphs.</EA>
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Hypergraph-based image retrieval for graph-based representation

Hypergraph-based image retrieval for graph-based representation

Source :

Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri