Scene Categorization Using Topic Model Based Hierarchical Conditional Random Fields

  • Vikram Garg
  • Ehtesham Hassan
  • Santanu Chaudhury
  • Madan Gopal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)


We propose a novel hierarchical framework for scene categorization. The scene representation is defined by latent topics extracted by Latent Dirichlet Allocation. The interaction of these topics across scene categories is learned by probabilistic graphical modelling. We use Conditional Random Fields in a hierarchical setting for discovering the global context of these topics. The learned random fields are further used for categorization of a new scene. The experimental results of the proposed framework is presented on standard datasets and on image collection obtained from the internet.


Scene categorization Latent dirichlet allocation Conditional random fields 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vikram Garg
    • 1
  • Ehtesham Hassan
    • 1
  • Santanu Chaudhury
    • 1
  • Madan Gopal
    • 1
  1. 1.Department of Electrical EngineeringIIT DelhiIndia

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