Abstraction and Generalization of 3D Structure for Recognition in Large Intra-Class Variation

  • Gowri Somanath
  • Chandra Kambhamettu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)


Humans have abstract models for object classes which helps recognize previously unseen instances, despite large intra-class variations. Also objects are grouped into classes based on their purpose. Studies in cognitive science show that humans maintain abstractions and certain specific features from the instances they observe. In this paper, we address the challenging task of creating a system which can learn such canonical models in a uniform manner for different classes. Using just a few examples the system creates a canonical model (COMPAS) per class, which is used to recognize classes with large intra-class variation (chairs, benches, sofas all belong to sitting class). We propose a robust representation and automatic scheme for abstraction and generalization. We quantitatively demonstrate improved recognition and classification accuracy over state-of-art 3D shape matching/classification method and discuss advantages over rule based systems.


Random Forest Gaussian Mixture Model Object Class Spherical Function Category Learning 
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 2011

Authors and Affiliations

  • Gowri Somanath
    • 1
  • Chandra Kambhamettu
    • 1
  1. 1.Video/Image Modeling and Synthesis (VIMS) Lab, Department of Computer and Information SciencesUniversity of DelawareNewarkUSA

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