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Learning Shape Prior Models for Object Matching

Tingting Jiang 1 Frédéric Jurie 1 Cordelia Schmid 1 
1 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : The aim of this work is to learn a shape prior model for an object class and to improve shape matching with the learned shape prior. Given images of example instances, we can learn a mean shape of the object class as well as the variations of non-affine and affine transformations separately based on the thin plate spline (TPS) parameterization. Unlike previous methods, for learning, we represent shapes by vector fields instead of features which makes our learning approach general. During shape matching, we inject the shape prior knowledge and make the matching result consistent with the training examples. This is achieved by an extension of the TPS-RPM algorithm which finds a closed form solution for the TPS transformation coherent with the learned transformations. We test our approach by using it to learn shape prior models for all the five object classes in the ETHZ Shape Classes. The results show that the learning accuracy is better than previous work and the learned shape prior models are helpful for object matching in real applications such as object classification.
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Submitted on : Monday, December 20, 2010 - 10:24:09 AM
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Tingting Jiang, Frédéric Jurie, Cordelia Schmid. Learning Shape Prior Models for Object Matching. CVPR 2009 - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2009, Miami, United States. pp.848-855, ⟨10.1109/CVPR.2009.5206568⟩. ⟨inria-00548646⟩



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