Paper Info
Title
Semantic indexing of multimedia content using textual and 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.
Year
DOI
Venue
2014
10.1504/IJAMC.2014.060496
IJAMC
Keywords
Field
DocType
semantic indexing,visual content,textual information,multimedia retrieval system,indexing multimedia content,visual information,indexing process,low-level information,multimedia information retrieval,different information,querying multimedia content,semantic concept,support vector machine,image classification,scale invariant feature transform,information retrieval,sift,svm
Ontology,Scale-invariant feature transform,Information retrieval,Computer science,Support vector machine,Multimedia information retrieval,Search engine indexing,WordNet,Contextual image classification,Multimedia,Visual Word
Journal
Volume
Issue
Citations 
5
2/3
0
PageRank 
References 
Authors
0.34
12
4
Name
Order
Citations
PageRank
Abdesalam Amrane100.34
Hakima Mellah2125.29
Rachid Aliradi310.70
Youssef Amghar400.34