Randomized Probabilistic Latent Semantic Analysis for Scene Recognition

  • Erik Rodner
  • Joachim Denzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


The concept of probabilistic Latent Semantic Analysis (pLSA) has gained much interest as a tool for feature transformation in image categorization and scene recognition scenarios. However, a major issue of this technique is overfitting. Therefore, we propose to use an ensemble of pLSA models which are trained using random fractions of the training data. We analyze empirically the influence of the degree of randomization and the size of the ensemble on the overall classification performance of a scene recognition task. A thoughtful evaluation shows the benefits of this approach compared to a single pLSA model.


Random Forest Recognition Rate Visual Word Latent Dirichlet Allocation Ensemble Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Erik Rodner
    • 1
  • Joachim Denzler
    • 1
  1. 1.Chair for Computer VisionFriedrich Schiller University of Jena 

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