Prior-based probabilistic latent semantic analysis for multimedia retrieval
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https://doi.org/10.1007/s11042-017-5247-z |
Metadades
Títol
Prior-based probabilistic latent semantic analysis for multimedia retrievalData de publicació
2017Editor
Springer VerlagISSN
1380-7501; 1573-7721Cita bibliogràfica
Fernandez-Beltran, R. & Pla, F. Multimed Tools Appl (2017). https://doi.org/10.1007/s11042-017-5247-zTipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://link.springer.com/article/10.1007/s11042-017-5247-zVersió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
Topic models have shown to be one of the most effective tools in Content-Based
Multimedia Retrieval (CBMR). However, the high computational learning cost together
with the huge expansion of multimedia collections ... [+]
Topic models have shown to be one of the most effective tools in Content-Based
Multimedia Retrieval (CBMR). However, the high computational learning cost together
with the huge expansion of multimedia collections limit the scalability of topic-based
CBMR systems in real-life multimedia applications. The present work pursues a twofold
objective. On the one hand, to study the effect of using clustering-based document reduction
schemes over standard topic models pLSA (probabilistic Latent Semantic Analysis)
and LDA (Latent Dirichlet Allocation). On the other hand, to develop a pLSA-based extension
oriented to integrate this reduction scheme within the own model in order to improve
the CBMR effectiveness. The experimental part of the work includes three different multimedia
databases, three ranking functions, four retrieval scenarios, three different numbers
of topics and ten document reduction levels. Experiments revealed that standard topic models
are highly sensitive to the document reduction level whereas the proposed model is able
to provide a competitive advantage within the content-based retrieval field. [-]
Publicat a
Multimed Tools Appl (2017). https://doi.org/10.1007/s11042-017-5247-zProyecto de investigación
ESP2013-48458-C4-3-P ; ESP2016-79503-C2-2-P ; PROMETEO-II/2014/062 ; P11B2014-09Drets d'accés
© Springer Science+Business Media, LLC 2017
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