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A Novel Method for Image Segmentation Based on Nature Inspired Algorithm

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Intelligent Computing in Bioinformatics (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8590))

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Abstract

Image segmentation is of great importance in the fields of computer vision, face recognition, medical imaging, digital libraries, and video retrieval. This paper presents a novel method for image segmentation based on a Hybrid particle swarm algorithm, which combines the advantages of swarm intelligence and the natural selection mechanism of artificial bee colony algorithm. Experimental results show that the proposed method can reach a higher quality adequate segmentation, reduce the CPU processing time and eliminate the particles falling into local minima.

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Author information

Authors and Affiliations

  1. Shenyang Institute of Automation Chinese Academy of Sciences, 110016, Shenyang, China

    Yang Liu, Kunyuan Hu, Yunlong Zhu & Hanning Chen

  2. Shenyang University, 110044, Shenyang, China

    Yang Liu

  3. University of Chinese Academy of Sciences, 100049, Beijing, China

    Yang Liu

Authors
  1. Yang Liu
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  2. Kunyuan Hu
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  3. Yunlong Zhu
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  4. Hanning Chen
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer Science and Engineering Inha University, Incheon, South Korea

    Kyungsook Han

  3. Department of Biotechnology, Indian Institute of Technology Madras, 600 036, Chennai, Tamilnadu, India

    Michael Gromiha

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© 2014 Springer International Publishing Switzerland

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Liu, Y., Hu, K., Zhu, Y., Chen, H. (2014). A Novel Method for Image Segmentation Based on Nature Inspired Algorithm. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_46

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  • DOI: https://doi.org/10.1007/978-3-319-09330-7_46

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09329-1

  • Online ISBN: 978-3-319-09330-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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