Multimedia Tools and Applications

, Volume 77, Issue 13, pp 16771–16793 | Cite as

Prior-based probabilistic latent semantic analysis for multimedia retrieval

  • Ruben Fernandez-Beltran
  • Filiberto Pla


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.


Information reduction Topic models Probabilistic latent semantic analysis Content-based multimedia retrieval 



This work was supported by the Spanish Ministry of Economy under the projects ESP2013-48458-C4-3-P and ESP2016-79503-C2-2-P, by Generalitat Valenciana through project PROMETEO-II/2014/062, and by Universitat Jaume I through project P11B2014-09.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institute of New Imaging TechnologiesUniversitat Jaume ICastellonSpain

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