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Generative Models for Labeling Multi-object Configurations in Images

  • Yali Amit
  • Alain Trouvé
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4170)

Abstract

We propose a generative approach to the problem of labeling images containing configurations of objects from multiple classes. The main building blocks are dense statistical models for individual objects. The models assume conditional independence of binary oriented edge variables conditional on a hidden instantiation parameter, which also determines an object support. These models are then be composed to form models for object configurations with various interactions including occlusion. Choosing the optimal configuration is entirely likelihood based and no decision boundaries need to be pre-learned. Training involves estimation of model parameters for each class separately. Both training and classification involve estimation of hidden pose variables which can be computationally intensive. We describe two levels of approximation which facilitate these computations: the Patchwork of Parts (POP) model and the coarse part based models (CPM). A concrete implementation of the approach is illustrated on the problem of reading zip-codes.

Keywords

Object Class Edge Type Coarse Model Reference Grid Bernoulli Model 
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 2006

Authors and Affiliations

  • Yali Amit
    • 1
  • Alain Trouvé
    • 2
  1. 1.Department of StatisticsUniversity of ChicagoChicagoUSA
  2. 2.CMLAENS-CachanCachan cedexFrance

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