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Object Detection Using Strongly-Supervised Deformable Part Models

  • Hossein Azizpour
  • Ivan Laptev
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

Deformable part-based models [1, 2] achieve state-of-the-art performance for object detection, but rely on heuristic initialization during training due to the optimization of non-convex cost function. This paper investigates limitations of such an initialization and extends earlier methods using additional supervision. We explore strong supervision in terms of annotated object parts and use it to (i) improve model initialization, (ii) optimize model structure, and (iii) handle partial occlusions. Our method is able to deal with sub-optimal and incomplete annotations of object parts and is shown to benefit from semi-supervised learning setups where part-level annotation is provided for a fraction of positive examples only. Experimental results are reported for the detection of six animal classes in PASCAL VOC 2007 and 2010 datasets. We demonstrate significant improvements in detection performance compared to the LSVM [1] and the Poselet [3] object detectors.

Keywords

Object Detection Minimum Span Tree Star Model Object Part Stochastic Gradient Descent 
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 2012

Authors and Affiliations

  • Hossein Azizpour
    • 1
  • Ivan Laptev
    • 2
  1. 1.Computer Vision and Active Perception Laboratory (CVAP)KTHSweden
  2. 2.WILLOW, Laboratoire d’Informatique de l’Ecole Normale SuperieureINRIAFrance

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