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Learning Structural Models in Multiple Projection Spaces

  • Conference paper
Image Analysis and Recognition (ICIAR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5627))

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Abstract

We present an Expectation-Maximization learning algorithm (E.M.) for estimating the parameters of partially-constrained Bayesian trees. The Bayesian trees considered here consist of an unconstrained subtree and a set of constrained subtrees. In this tree structure, constraints are imposed on some of the parameters of the parametrized conditional distributions, such that all conditional distributions within the same subtree share the same constraint. We propose a learning method that uses the unconstrained subtree to guide the process of discovering a set of relevant constrained tree substructures. Substructure discovery and constraint enforcement are simultaneously accomplished using an E.M. algorithm. We show how our tree substructure discovery method can be applied to the problem of learning representative pose models from a set of unsegmented video sequences. Our experiments demonstrate the potential of the proposed method for human motion classification.

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Author information

Authors and Affiliations

  1. Computer Vision and Bio-Inspired Computing Laboratory Department of Computer Sciences, Florida Institute of Technology, Melbourne, FL, 32901, USA

    Roman Filipovych & Eraldo Ribeiro

Authors
  1. Roman Filipovych
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  2. Eraldo Ribeiro
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2009 Springer-Verlag Berlin Heidelberg

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Filipovych, R., Ribeiro, E. (2009). Learning Structural Models in Multiple Projection Spaces. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_61

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  • DOI: https://doi.org/10.1007/978-3-642-02611-9_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02610-2

  • Online ISBN: 978-3-642-02611-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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