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A Simple Stochastic Algorithm for Structural Features Learning

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Book cover Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9010))

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Abstract

A conceptually very simple unsupervised algorithm for learning structure in the form of a hierarchical probabilistic model is described in this paper. The proposed probabilistic model can easily work with any type of image primitives such as edge segments, non-max-suppressed filter set responses, texels, distinct image regions, SIFT features, etc., and is even capable of modelling non-rigid and/or visually variable objects. The model has recursive form and consists of sets of simple and gradually growing sub-models that are shared and learned individually in layers. The proposed probabilistic framework enables to exactly compute the probability of presence of a certain model, regardless on which layer it actually is. All these learned models constitute a rich set of independent structure elements of variable complexity that can be used as features in various recognition tasks.

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Notes

  1. 1.

    Training and classification of same object classes in different datasets.

  2. 2.

    For the sake of clarity, the unnecessary indices are ommited.

  3. 3.

    The grouping is actually generated randomly.

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Acknowledgement

The authors were supported by the Czech Science Foundation under the Project P103/12/1578.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Department of Cybernetics, Center for Machine Perception, Czech Technical University in Prague, Prague, Czech Republic

    Jan Mačák & Ondřej Drbohlav

Authors
  1. Jan Mačák
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  2. Ondřej Drbohlav
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Corresponding author

Correspondence to Jan Mačák .

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Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C. V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Mačák, J., Drbohlav, O. (2015). A Simple Stochastic Algorithm for Structural Features Learning. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-16634-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16633-9

  • Online ISBN: 978-3-319-16634-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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