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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)

SSPR /SPR 2012: Structural, Syntactic, and Statistical Pattern Recognition pp 153–161Cite as

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A Clustering-Based Ensemble Technique for Shape Decomposition

A Clustering-Based Ensemble Technique for Shape Decomposition

  • Sergej Lewin24,
  • Xiaoyi Jiang24 &
  • Achim Clausing24 
  • Conference paper
  • 2423 Accesses

  • 3 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7626)

Abstract

Ensemble techniques have been very successful in pattern recognition. In this work we investigate ensemble solution for shape decomposition. A clustering-based approach is proposed to determine a final decomposition from an ensemble of input decompositions. A recently published performance evaluation framework consisting of a benchmark database with manual ground truth together with evaluation measures is used to demonstrate the benefit of the proposed ensemble technique.

Keywords

  • Dissimilarity Measure
  • Ensemble Technique
  • Benchmark Database
  • Majority Vote Rule
  • Majority Vote Scheme

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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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Einsteinstrasse 62, 49149, Münster, Germany

    Sergej Lewin, Xiaoyi Jiang & Achim Clausing

Authors
  1. Sergej Lewin
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  2. Xiaoyi Jiang
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  3. Achim Clausing
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Auckland, Private Bag 92019, 1142, Auckland, New Zealand

    Georgy Gimel’farb

  2. Department of Computer Science, University of York, Deramore Lane, YO10 5GH, York, UK

    Edwin Hancock

  3. Institute of Media and Information Technology, Chiba University, Yayoi-cho 1-33, 263-8522, Inage-ku, Chiba, Japan

    Atsushi Imiya

  4. Technische Universität/Fraunhofer IGD, Fraunhoferstraße 5, 64283, Darmstadt, Germany

    Arjan Kuijper

  5. Graduate School of Information Science and Technology, Hokkaido University, 060-0814, Sapporo, Japan

    Mineichi Kudo

  6. Graduate School of Engineering, Tohoku University, 6-6-05 Aoba, Aramaki, Aoba-ku, 980-8579, Sendai, Miyagi, Japan

    Shinichiro Omachi

  7. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Terry Windeatt

  8. C&C Innovation Research Laboratories, NEC Corporation, 8916-47 Takayama-cho, Ikoma-Shi, Nara, Japan

    Keiji Yamada

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Lewin, S., Jiang, X., Clausing, A. (2012). A Clustering-Based Ensemble Technique for Shape Decomposition. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_17

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  • DOI: https://doi.org/10.1007/978-3-642-34166-3_17

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  • Print ISBN: 978-3-642-34165-6

  • Online ISBN: 978-3-642-34166-3

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