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Object Detection by Simple Fuzzy Classifiers Generated by Boosting

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Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

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Abstract

Finding key points based on SURF and SIFT and size of their vector reduction is a classical approach for object recognition systems. In this paper we present a new framework for object recognition based on generating simple fuzzy classifiers using key points and boosting meta learning to distinguish between one known class and other classes. We tested proposed approach on a known image dataset.

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Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Czȩstochowa University of Technology, al. Armii Krajowej 36, 42-200, Czȩstochowa, Poland

    Marcin Gabryel, Marcin Korytkowski, Rafał Scherer & Leszek Rutkowski

Authors
  1. Marcin Gabryel
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  2. Marcin Korytkowski
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  3. Rafał Scherer
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  4. Leszek Rutkowski
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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Gabryel, M., Korytkowski, M., Scherer, R., Rutkowski, L. (2013). Object Detection by Simple Fuzzy Classifiers Generated by Boosting. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_49

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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