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Constructing Category Hierarchies for Visual Recognition

  • Marcin Marszałek
  • Cordelia Schmid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)

Abstract

Class hierarchies are commonly used to reduce the complexity of the classification problem. This is crucial when dealing with a large number of categories. In this work, we evaluate class hierarchies currently constructed for visual recognition. We show that top-down as well as bottom-up approaches, which are commonly used to automatically construct hierarchies, incorporate assumptions about the separability of classes. Those assumptions do not hold for visual recognition of a large number of object categories. We therefore propose a modification which is appropriate for most top-down approaches. It allows to construct class hierarchies that postpone decisions in the presence of uncertainty and thus provide higher recognition accuracy. We also compare our method to a one-against-all approach and show how to control the speed-for-accuracy trade-off with our method. For the experimental evaluation, we use the Caltech-256 visual object classes dataset and compare to state-of-the-art methods.

Keywords

Support Vector Machine Image Representation Object Category Visual Recognition Class Hierarchy 
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

  • Marcin Marszałek
    • 1
  • Cordelia Schmid
    • 1
  1. 1.INRIA Grenoble, LEAR, LJK 

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