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A New Variant of the Optimum-Path Forest Classifier

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Advances in Visual Computing (ISVC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5358))

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Abstract

We have shown a supervised approach for pattern classification, which interprets the training samples as nodes of a complete arc-weighted graph and computes an optimum-path forest rooted at some of the closest samples between distinct classes. A new sample is classified by the label of the root which offers to it the optimum path. We propose a variant, in which the training samples are the nodes of a graph, whose the arcs are the k-nearest neighbors in the feature space. The graph is weighted on the nodes by their probability density values (pdf) and the optimum-path forest is rooted at the maxima of the pdf. The best value of k is computed by the maximum accuracy of classification in the training set. A test sample is assigned to the class of the maximum, which offers to it the optimum path. Preliminary results have shown that the proposed approach can outperform the previous one and the SVM classifier in some datasets.

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Author information

Authors and Affiliations

  1. Institute of Computing, State University of Campinas, Av. Albert Einstein 1216, Campinas, São Paulo, Brazil

    João P. Papa & Alexandre X. Falcão

Authors
  1. João P. Papa
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  2. Alexandre X. Falcão
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

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© 2008 Springer-Verlag Berlin Heidelberg

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Papa, J.P., Falcão, A.X. (2008). A New Variant of the Optimum-Path Forest Classifier. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_89

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  • DOI: https://doi.org/10.1007/978-3-540-89639-5_89

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89638-8

  • Online ISBN: 978-3-540-89639-5

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