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Localizing Objects While Learning Their Appearance

  • Thomas Deselaers
  • Bogdan Alexe
  • Vittorio Ferrari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)

Abstract

Learning a new object class from cluttered training images is very challenging when the location of object instances is unknown. Previous works generally require objects covering a large portion of the images. We present a novel approach that can cope with extensive clutter as well as large scale and appearance variations between object instances. To make this possible we propose a conditional random field that starts from generic knowledge and then progressively adapts to the new class. Our approach simultaneously localizes object instances while learning an appearance model specific for the class. We demonstrate this on the challenging Pascal VOC 2007 dataset. Furthermore, our method enables to train any state-of-the-art object detector in a weakly supervised fashion, although it would normally require object location annotations.

Keywords

Training Image Object Class Appearance Model Target Class Learning Stage 
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

  • Thomas Deselaers
    • 1
  • Bogdan Alexe
    • 1
  • Vittorio Ferrari
    • 1
  1. 1.Computer Vision LaboratoryETH ZurichZurichSwitzerland

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