Incremental probabilistic Latent Semantic Analysis for video retrieval
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Título
Incremental probabilistic Latent Semantic Analysis for video retrievalFecha de publicación
2015-06xmlui.dri2xhtml.METS-1.0.item-edition
PreprintEditor
ElsevierCita bibliográfica
FERNANDEZ-BELTRAN, Ruben; PLA, Filiberto. Incremental probabilistic Latent Semantic Analysis for video retrieval. Image and Vision Computing, 2015, vol. 38, p. 1-12.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0262885615000335Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Recent research trends in Content-based Video Retrieval have shown topic models as an effective tool to deal
with the semantic gap challenge. In this scenario, this paper has a dual target: (1) it is aimed at studying ... [+]
Recent research trends in Content-based Video Retrieval have shown topic models as an effective tool to deal
with the semantic gap challenge. In this scenario, this paper has a dual target: (1) it is aimed at studying how
the use of different topic models (pLSA, LDA and FSTM) affects video retrieval performance; (2) a novel incremental
topic model (IpLSA) is presented in order to cope with incremental scenarios in an effective and efficient
way. A comprehensive comparison among these four topic models using two different retrieval systems and two
reference benchmarking video databases is provided. Experiments revealed that pLSA is the best model in sparse
conditions, LDA tend to outperform the rest of the models in a dense space and IpLSA is able to work properly in
both cases. [-]
Publicado en
Image and Vision Computing, 2015, vol. 38Derechos de acceso
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