Towards Learning Hierarchical Compositional Models in the Presence of Clutter

  • Jan Mačák
  • Ondřej Drbohlav
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


Our goal is to identify hierarchical compositional models from highly cluttered data. The data to learn from are assumed to be imperfect in two respects. Firstly, large portion of the data is coming from background clutter. Secondly, data generated by a recursive compositional model are subject to random replacements of correct descendants by randomly chosen ones at every level of the hierarchy. In this paper, we study the limits and capabilities of an approach which is based on likelihood maximization. The algorithm makes explicit probabilistic assignments of individual data to compositional model and background clutter. It uses these assignments to effectively focus on the data coming from the compositional model and iteratively estimate their compositional structure.


Ground Truth Compositional Model Minimum Description Length Background Clutter Minimum Description Length Principle 
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 2013

Authors and Affiliations

  • Jan Mačák
    • 1
  • Ondřej Drbohlav
    • 1
  1. 1.Center for Machine Perception (CMP), Department of CyberneticsCzech Technical University in PragueCzech Republic

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