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Discovering Multipart Appearance Models from Captioned Images

  • Michael Jamieson
  • Yulia Eskin
  • Afsaneh Fazly
  • Suzanne Stevenson
  • Sven Dickinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

Even a relatively unstructured captioned image set depicting a variety of objects in cluttered scenes contains strong correlations between caption words and repeated visual structures. We exploit these correlations to discover named objects and learn hierarchical models of their appearance. Revising and extending a previous technique for finding small, distinctive configurations of local features, our method assembles these co-occurring parts into graphs with greater spatial extent and flexibility. The resulting multipart appearance models remain scale, translation and rotation invariant, but are more reliable detectors and provide better localization. We demonstrate improved annotation precision and recall on datasets to which the non-hierarchical technique was previously applied and show extended spatial coverage of detected objects.

Keywords

Part Model Appearance Model Image Annotation Part Detection National Hockey League 
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 2010

Authors and Affiliations

  • Michael Jamieson
    • 1
  • Yulia Eskin
    • 1
  • Afsaneh Fazly
    • 1
  • Suzanne Stevenson
    • 1
  • Sven Dickinson
    • 1
  1. 1.University of Toronto 

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