Latent Pyramidal Regions for Recognizing Scenes

  • Fereshteh Sadeghi
  • Marshall F. Tappen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7576)


In this paper we propose a simple but efficient image representation for solving the scene classification problem. Our new representation combines the benefits of spatial pyramid representation using nonlinear feature coding and latent Support Vector Machine (LSVM) to train a set of Latent Pyramidal Regions (LPR). Each of our LPRs captures a discriminative characteristic of the scenes and is trained by searching over all possible sub-windows of the images in a latent SVM training procedure. Each LPR is represented in a spatial pyramid and uses non-linear locality constraint coding for learning both shape and texture patterns of the scene. The final response of the LPRs form a single feature vector which we call the LPR representation and can be used for the classification task. We tested our proposed scene representation model in three datasets which contain a variety of scene categories (15-Scenes, UIUC-Sports and MIT-indoor). Our LPR representation obtains state-of-the-art results on all these datasets which shows that it can simultaneously model the global and local scene characteristics in a single framework and is general enough to be used for both indoor and outdoor scene classification.


Feature Vector Sparse Code Region Detector Image Descriptor Scene Recognition 
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 2012

Authors and Affiliations

  • Fereshteh Sadeghi
    • 1
  • Marshall F. Tappen
    • 1
  1. 1.University of Central FloridaOrlandoUSA

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