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Modularity Segmentation

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Book cover Neural Information Processing (ICONIP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8227))

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Abstract

Graph-partition based algorithms are widely used for image segmentation. We propose an improved graph-partition segmentation method based on a key notion from complex network analysis: partition modularity. In particular, we show how optimizing the modularity measure can automatically determine the number of segments as well as their respective structure—greatly reducing the level of human intervention in the image segmentation process. We furthermore develop an efficient spectral approach that allows for a fast segmentation procedure. The proposed method is simple, efficient, and provides a practical tool for analyzing real-world images.

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Author information

Authors and Affiliations

  1. Macao Polytechnic Institute, Rua de Luís Gonzaga, Macao SAR, China

    Wenye Li

Authors
  1. Wenye Li
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Editor information

Editors and Affiliations

  1. Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  2. The University of Tokyo, 7-3-1 Hongo, 113-8656, Bunkyo-ku, Tokyo, Japan

    Akira Hirose

  3. Institute of Automation, Key Laboratory of Complex Systems and Intelligence Science, Chinese Academy of Sciences, 100190, Beijing, China

    Zeng-Guang Hou

  4. Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, 440-746, Suwon, Korea

    Rhee Man Kil

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© 2013 Springer-Verlag Berlin Heidelberg

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Li, W. (2013). Modularity Segmentation. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_13

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  • DOI: https://doi.org/10.1007/978-3-642-42042-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-42041-2

  • Online ISBN: 978-3-642-42042-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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