AustralieFrV1, PascalFrancis, Corpus, bibRecord, 000059

Semantic indexing of multimedia content using textual and visual information

Identifieur interne : 000059 ( PascalFrancis/Corpus ); précédent : 000058; suivant : 000060

Semantic indexing of multimedia content using textual and visual information

Auteurs : Abdesalam Amrane ; Hakima Mellah ; Rachid Aliradi ; Youssef Amghar

Source :

International journal of advanced media and communication [ 1462-4613 ] ; 2014.

RBID : Pascal:15-0028757

Descripteurs français

Pascal (Inist)
- Indexation, Multimédia, Donnée textuelle, Information visuelle, Base donnée multimédia, Recherche information, Texte, Gestion contenu, Interrogation base donnée, Linguistique, Ontologie, Traitement image, Sémantique, Mot clé, Lexique, Analyse conceptuelle, Annotation, Classification à vaste marge, Représentation parcimonieuse, Capteur multiple, Analyse sémantique, ., Recherche par contenu, Classification image, Appariement image.

English descriptors

KwdEn :
- Annotation, Conceptual analysis, Content management, Content-based retrieval, Database query, Image classification, Image matching, Image processing, Indexing, Information retrieval, Keyword, Lexicon, Linguistics, Multimedia, Multimedia databases, Multisensor, Ontology, Semantic analysis, Semantics, Sparse representation, Text, Textual data, Vector support machine, Visual information.

Abstract

The challenge in multimedia information retrieval remains in the indexing process, an active search area. There are three fundamental techniques for indexing multimedia content: using textual information, using low-level information and combining different information extracted from multimedia. Each approach has its advantages and disadvantages as well to improve multimedia retrieval systems. The recent works are oriented towards multimodal approaches. In this paper, we propose an approach that combines the surrounding text with the information extracted from the visual content of multimedia and represented in the same repository in order to allow querying multimedia content based on keywords or concepts. Each word contained in queries or in description of multimedia is disambiguated using the WordNet ontology in order to define its semantic concept. Support vector machines (SVMs) are used for image classification in one of the defined semantic concept based on SIFT (scale invariant feature transform) descriptors.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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C01	`01`		`ENG`	@0 The challenge in multimedia information retrieval remains in the indexing process, an active search area. There are three fundamental techniques for indexing multimedia content: using textual information, using low-level information and combining different information extracted from multimedia. Each approach has its advantages and disadvantages as well to improve multimedia retrieval systems. The recent works are oriented towards multimodal approaches. In this paper, we propose an approach that combines the surrounding text with the information extracted from the visual content of multimedia and represented in the same repository in order to allow querying multimedia content based on keywords or concepts. Each word contained in queries or in description of multimedia is disambiguated using the WordNet ontology in order to define its semantic concept. Support vector machines (SVMs) are used for image classification in one of the defined semantic concept based on SIFT (scale invariant feature transform) descriptors.
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Format Inist (serveur)

NO :	PASCAL 15-0028757 INIST
ET :	Semantic indexing of multimedia content using textual and visual information
AU :	AMRANE (Abdesalam); MELLAH (Hakima); ALIRADI (Rachid); AMGHAR (Youssef); WAI CHI FANG; KIM (Tai-hoon); RAMOS (Carlos); MOHAMMED (Sabah); GERVASI (Osvaldo); STOICA (Adrian)
AF :	Research Center on Scientific and Technical Information (CERIST)/Ben Aknoun, Algiers/Algérie (1 aut., 2 aut., 3 aut.); University of Lyon, CNRS, INSA-Lyon, LIRIS/UMR5205, 69621/France (4 aut.); Department of Electronics Engineering, National Chiao Tung University, 1001 Ta Hsueh Road/Hinschu, Taiwan 300/Taïwan (1 aut.); School of Computing and Information Science, University of Tasmania, Australia, Centenary Building, Room 350, Private Bag 87/Hobart, TAS 7001/Australie (2 aut.); Instituto Politécnico do Porto, Rua Dr. António Bernardino de Almeida, 431/Porto 4200-072/Portugal (3 aut.); Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay/Ontario P7B 5E1/Canada (4 aut.); Department of Mathematics and Computer Science, University of Perugia/106123 Perugia/Italie (5 aut.); NASA JPL, M/S 303-300, 4800 Oak Grove Drive/Pasadena, CA 91109/Etats-Unis (6 aut.)
DT :	Publication en série; Niveau analytique
SO :	International journal of advanced media and communication; ISSN 1462-4613; Suisse; Da. 2014; Vol. 5; No. 2-3; Pp. 182-194; Bibl. 1 p.
LA :	Anglais
EA :	The challenge in multimedia information retrieval remains in the indexing process, an active search area. There are three fundamental techniques for indexing multimedia content: using textual information, using low-level information and combining different information extracted from multimedia. Each approach has its advantages and disadvantages as well to improve multimedia retrieval systems. The recent works are oriented towards multimodal approaches. In this paper, we propose an approach that combines the surrounding text with the information extracted from the visual content of multimedia and represented in the same repository in order to allow querying multimedia content based on keywords or concepts. Each word contained in queries or in description of multimedia is disambiguated using the WordNet ontology in order to define its semantic concept. Support vector machines (SVMs) are used for image classification in one of the defined semantic concept based on SIFT (scale invariant feature transform) descriptors.
CC :	001D02C03; 001D02B07D; 001D02B04; 001D02B07B
FD :	Indexation; Multimédia; Donnée textuelle; Information visuelle; Base donnée multimédia; Recherche information; Texte; Gestion contenu; Interrogation base donnée; Linguistique; Ontologie; Traitement image; Sémantique; Mot clé; Lexique; Analyse conceptuelle; Annotation; Classification à vaste marge; Représentation parcimonieuse; Capteur multiple; Analyse sémantique; .; Recherche par contenu; Classification image; Appariement image
ED :	Indexing; Multimedia; Textual data; Visual information; Multimedia databases; Information retrieval; Text; Content management; Database query; Linguistics; Ontology; Image processing; Semantics; Keyword; Lexicon; Conceptual analysis; Annotation; Vector support machine; Sparse representation; Multisensor; Semantic analysis; Content-based retrieval; Image classification; Image matching
SD :	Indización; Multimedia; Dato textual; Información visual; Búsqueda información; Texto; Gestión contenido; Interrogación base datos; Linguística; Ontología; Procesamiento imagen; Semántica; Palabra clave; Léxico; Análisis conceptual; Anotación; Máquina ejemplo soporte; Representación parsimoniosa; Multisensor; Análisis semántico; Búsqueda por Contenidos; Clasificación de imágenes; reconocimiento de patrones en imágenes
LO :	INIST-27778.354000504548010080
ID :	15-0028757

Links to Exploration step

Pascal:15-0028757

Le document en format XML

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<front><div type="abstract" xml:lang="en">The challenge in multimedia information retrieval remains in the indexing process, an active search area. There are three fundamental techniques for indexing multimedia content: using textual information, using low-level information and combining different information extracted from multimedia. Each approach has its advantages and disadvantages as well to improve multimedia retrieval systems. The recent works are oriented towards multimodal approaches. In this paper, we propose an approach that combines the surrounding text with the information extracted from the visual content of multimedia and represented in the same repository in order to allow querying multimedia content based on keywords or concepts. Each word contained in queries or in description of multimedia is disambiguated using the WordNet ontology in order to define its semantic concept. Support vector machines (SVMs) are used for image classification in one of the defined semantic concept based on SIFT (scale invariant feature transform) descriptors.</div>
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<fC01 i1="01" l="ENG"><s0>The challenge in multimedia information retrieval remains in the indexing process, an active search area. There are three fundamental techniques for indexing multimedia content: using textual information, using low-level information and combining different information extracted from multimedia. Each approach has its advantages and disadvantages as well to improve multimedia retrieval systems. The recent works are oriented towards multimodal approaches. In this paper, we propose an approach that combines the surrounding text with the information extracted from the visual content of multimedia and represented in the same repository in order to allow querying multimedia content based on keywords or concepts. Each word contained in queries or in description of multimedia is disambiguated using the WordNet ontology in order to define its semantic concept. Support vector machines (SVMs) are used for image classification in one of the defined semantic concept based on SIFT (scale invariant feature transform) descriptors.</s0>
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<ET>Semantic indexing of multimedia content using textual and visual information</ET>
<AU>AMRANE (Abdesalam); MELLAH (Hakima); ALIRADI (Rachid); AMGHAR (Youssef); WAI CHI FANG; KIM (Tai-hoon); RAMOS (Carlos); MOHAMMED (Sabah); GERVASI (Osvaldo); STOICA (Adrian)</AU>
<AF>Research Center on Scientific and Technical Information (CERIST)/Ben Aknoun, Algiers/Algérie (1 aut., 2 aut., 3 aut.); University of Lyon, CNRS, INSA-Lyon, LIRIS/UMR5205, 69621/France (4 aut.); Department of Electronics Engineering, National Chiao Tung University, 1001 Ta Hsueh Road/Hinschu, Taiwan 300/Taïwan (1 aut.); School of Computing and Information Science, University of Tasmania, Australia, Centenary Building, Room 350, Private Bag 87/Hobart, TAS 7001/Australie (2 aut.); Instituto Politécnico do Porto, Rua Dr. António Bernardino de Almeida, 431/Porto 4200-072/Portugal (3 aut.); Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay/Ontario P7B 5E1/Canada (4 aut.); Department of Mathematics and Computer Science, University of Perugia/106123 Perugia/Italie (5 aut.); NASA JPL, M/S 303-300, 4800 Oak Grove Drive/Pasadena, CA 91109/Etats-Unis (6 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>International journal of advanced media and communication; ISSN 1462-4613; Suisse; Da. 2014; Vol. 5; No. 2-3; Pp. 182-194; Bibl. 1 p.</SO>
<LA>Anglais</LA>
<EA>The challenge in multimedia information retrieval remains in the indexing process, an active search area. There are three fundamental techniques for indexing multimedia content: using textual information, using low-level information and combining different information extracted from multimedia. Each approach has its advantages and disadvantages as well to improve multimedia retrieval systems. The recent works are oriented towards multimodal approaches. In this paper, we propose an approach that combines the surrounding text with the information extracted from the visual content of multimedia and represented in the same repository in order to allow querying multimedia content based on keywords or concepts. Each word contained in queries or in description of multimedia is disambiguated using the WordNet ontology in order to define its semantic concept. Support vector machines (SVMs) are used for image classification in one of the defined semantic concept based on SIFT (scale invariant feature transform) descriptors.</EA>
<CC>001D02C03; 001D02B07D; 001D02B04; 001D02B07B</CC>
<FD>Indexation; Multimédia; Donnée textuelle; Information visuelle; Base donnée multimédia; Recherche information; Texte; Gestion contenu; Interrogation base donnée; Linguistique; Ontologie; Traitement image; Sémantique; Mot clé; Lexique; Analyse conceptuelle; Annotation; Classification à vaste marge; Représentation parcimonieuse; Capteur multiple; Analyse sémantique; .; Recherche par contenu; Classification image; Appariement image</FD>
<ED>Indexing; Multimedia; Textual data; Visual information; Multimedia databases; Information retrieval; Text; Content management; Database query; Linguistics; Ontology; Image processing; Semantics; Keyword; Lexicon; Conceptual analysis; Annotation; Vector support machine; Sparse representation; Multisensor; Semantic analysis; Content-based retrieval; Image classification; Image matching</ED>
<SD>Indización; Multimedia; Dato textual; Información visual; Búsqueda información; Texto; Gestión contenido; Interrogación base datos; Linguística; Ontología; Procesamiento imagen; Semántica; Palabra clave; Léxico; Análisis conceptual; Anotación; Máquina ejemplo soporte; Representación parsimoniosa; Multisensor; Análisis semántico; Búsqueda por Contenidos; Clasificación de imágenes; reconocimiento de patrones en imágenes</SD>
<LO>INIST-27778.354000504548010080</LO>
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Semantic indexing of multimedia content using textual and visual information

Semantic indexing of multimedia content using textual and visual information

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