Inference and Learning with Hierarchical Shape Models

  • Authors:

  • Iasonas Kokkinos;Alan Yuille

  • Affiliations:

  • Department of Applied Mathematics, Ecole Centrale Paris and Equipe Galen, INRIA-Saclay, France;Department of Statistics and Computer Science, University of California at Los Angeles, Los Angeles, USA

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2011

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Abstract

In this work we introduce a hierarchical representation for object detection. We represent an object in terms of parts composed of contours corresponding to object boundaries and symmetry axes; these are in turn related to edge and ridge features that are extracted from the image.We propose a coarse-to-fine algorithm for efficient detection which exploits the hierarchical nature of the model. This provides a tractable framework to combine bottom-up and top-down computation. We learn our models from training images where only the bounding box of the object is provided. We automate the decomposition of an object category into parts and contours, and discriminatively learn the cost function that drives the matching of the object to the image using Multiple Instance Learning.Using shape-based information, we obtain state-of-the-art localization results on the UIUC and ETHZ datasets.