Latent Topics-based Relevance Feedback for Video Retrieval
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comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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INVESTIGACIONMetadatos
Título
Latent Topics-based Relevance Feedback for Video RetrievalFecha de publicación
2015Editor
ElsevierISSN
0031-3203Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0031320315003386Versión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
This paper presents a novel Content-Based Video Retrieval approach in order to cope with the semantic gap challenge by means of latent topics. Firstly, a supervised topic model is proposed to transform the classical ... [+]
This paper presents a novel Content-Based Video Retrieval approach in order to cope with the semantic gap challenge by means of latent topics. Firstly, a supervised topic model is proposed to transform the classical retrieval approach into a class discovery problem. Subsequently, a new probabilistic ranking function is deduced from that model to tackle the semantic gap between low-level features and high-level concepts. Finally, a short-term relevance feedback scheme is defined where queries can be initialised with samples from inside or outside the database. Several retrieval simulations have been carried out using three databases and seven different ranking functions to test the performance of the presented approach. Experiments revealed that the proposed ranking function is able to provide a competitive advantage within the content-based retrieval field. [-]
Publicado en
Pattern Recognition Volume 51, March 2016, Pages 72–84Derechos de acceso
© 2015 Elsevier Ltd. All rights reserved.
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info:eu-repo/semantics/openAccess
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info:eu-repo/semantics/openAccess
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