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A Self-adaptive Segmentation Method by Fusion of Multi-color Space Components

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Artificial Intelligence and Computational Intelligence (AICI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7530))

Abstract

This paper presents a new, simple, and efficient segmentation approach. Firstly, choose the best segmentation components among six different color spaces. Then, Histogram and SFCM techniques are applied for initialization of segmentation. Finally, fuse the segmentation results and merge similar regions. Extensive experiments have been taken on Berkeley image database by using the proposed algorithm. The results show that, compared with some classical segmentation algorithms, our method could achieve better image partitioning and better performance.

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Author information

Authors and Affiliations

  1. Department of Information Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, 200234, China

    Kun Chen, Yan Ma & Jun Liu

  2. Department of Mathematic & Science, Shanghai Normal University, Shanghai, 200234, China

    Shun-bao Li

Authors
  1. Kun Chen
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  2. Yan Ma
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  3. Jun Liu
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  4. Shun-bao Li
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Editor information

Editors and Affiliations

  1. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  2. Deaprtment of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

  3. School of Business Information Technology and Logistics, RMIT University, City Campus, 124 la Tobre Street, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  4. School of Electronics and Information, Tongji University, 201804, Shanghai, China

    Duoqian Miao

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© 2012 Springer-Verlag Berlin Heidelberg

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Chen, K., Ma, Y., Liu, J., Li, Sb. (2012). A Self-adaptive Segmentation Method by Fusion of Multi-color Space Components. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_47

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  • DOI: https://doi.org/10.1007/978-3-642-33478-8_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33477-1

  • Online ISBN: 978-3-642-33478-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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