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Scene Discovery by Matrix Factorization

  • Nicolas Loeff
  • Ali Farhadi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)

Abstract

What constitutes a scene? Defining a meaningful vocabulary for scene discovery is a challenging problem that has important consequences for object recognition. We consider scenes to depict correlated objects and present visual similarity. We introduce a max-margin factorization model that finds a low dimensional subspace with high discriminative power for correlated annotations. We postulate this space should allow us to discover a large number of scenes in unsupervised data; we show scene discrimination results on par with supervised approaches. This model also produces state of the art word prediction results including good annotation completion.

Keywords

Matrix Factorization Latent Dirichlet Allocation Model Supervise Approach Word Annotation Auxiliary Task 
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 2008

Authors and Affiliations

  • Nicolas Loeff
    • 1
  • Ali Farhadi
    • 1
  1. 1.University of Illinois at Urbana-ChampaignUrbana

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