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GPU Implementation of MCE Approach to Finding Near Neighbourhoods

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Book cover Rough Sets and Knowledge Technology (RSKT 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8171))

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Abstract

This paper presents a parallel version of the Maximal Clique Enumeration (MCE) approach for discovering tolerance classes. Finding such classes is a computationally complex problem, especially in the case of large data sets or in content-based retrieval applications(CBIR). The GPU implementation is an extension of earlier work by the authors on finding efficient methods for computing tolerance classes in images. The experimental results demonstrate that the GPU-based MCE algorithm is faster than the serial MCE implementation and can perform computations with higher values of tolerance ε.

This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) grants 194376, 418413 and 185986. Tariq Alusaifeer has been supported by Government of Saudi Arabia.The authors wish to thank Prof. J. Wang for sharing the SIMPlicity image database.

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Author information

Authors and Affiliations

  1. Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, R3B 2E9, Canada

    Tariq Alusaifeer, Sheela Ramanna & Christopher J. Henry

  2. Computational Intelligence Laboratory, ECE Department, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada

    James Peters

Authors
  1. Tariq Alusaifeer
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  2. Sheela Ramanna
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  3. Christopher J. Henry
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  4. James Peters
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Editor information

Editors and Affiliations

  1. Saint Mary’s University, B3H 3C3, Halifax, NS, Canada

    Pawan Lingras

  2. Maria Curie-Skłodowska University, Lublin, Poland

    Marcin Wolski

  3. University of Granada, Spain

    Chris Cornelis

  4. Indian Statistical Institute, 700108, Kolkata, India

    Sushmita Mitra

  5. University of Warsaw, 02-097, Warsaw, Poland

    Piotr Wasilewski

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Alusaifeer, T., Ramanna, S., Henry, C.J., Peters, J. (2013). GPU Implementation of MCE Approach to Finding Near Neighbourhoods. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_24

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  • DOI: https://doi.org/10.1007/978-3-642-41299-8_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41298-1

  • Online ISBN: 978-3-642-41299-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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