On the selection of MPEG-7 visual descriptors and their level of detail for nature disaster video sequences classification
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2007Citation
10.1007/978-3-540-77051-0_6
Semantic Multimedia: Second International Conference on Semantic and Digital Media Technologies, SAMT 2007, Genoa, Italy, December 5-7, 2007. Proceedings . Lecture Notes in Computer Science, Volumen 4816. Springer, 2007. 70-73.
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-540-77033-6 (print); 978-3-540-77051-0 (online)DOI
10.1007/978-3-540-77051-0_6Funded by
The work presented in this paper was partially supported by the European Commision under contractFP6-027685 MESH, and by the Spanish Government under project TIN2004- 07860-C02 (MEDUSA) and by the Comunidad de Madrid under project S-0505/ TIC-0223 (ProMultiDis- CM). Evaggelos Spyrou is funded by PENED 2003 – Project Ontomedia 03ED475.Project
info:eu-repo/grantAgreement/EC/FP6/027685; Comunidad de Madrid. S2006/TIC-0233/PROMULTIDISEditor's Version
http://dx.doi.org/10.1007/978-3-540-77051-0_6Subjects
Image Classification; Semantic Retrieval; Visual Descriptors; Informática; TelecomunicacionesNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-77051-0_6Proceedings of Second International Conference on Semantic and Digital Media Technologies, SAMT 2007, Genoa, Italy, December 5-7, 2007.
Rights
© 2007 IEEEAbstract
In this paper, we present a study on the discrimination capabilities of colour, texture and shape MPEG-7 visual descriptors, within the context of video sequences. The target is to facilitate the recognition of certain visual cues which would then allow the classification of natural disaster-related concepts. Low-level visual features are extracted using the MPEG-7 “eXperimentation Module” (XM). The extraction times associated to the levels of detail of the descriptors are measured. The pattern sets obtained as combination of significant levels of detail of different descriptors are the input to a Support Vector Machine (SVM), resulting on the classification accuracies. Preliminary results indicate that this approach could be useful for the implementation of real-time spatial regions classifiers.
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Google Scholar:Molina Vela, Javier
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Spyrou, Evaggelos
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Sofou, Natasa
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Martínez Sánchez, José María
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