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A Color Image Segmentation Algorithm by Using Region and Edge Information

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AI 2005: Advances in Artificial Intelligence (AI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3809))

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Abstract

A novel segmentation algorithm for natural color image is proposed. Fibonacci Lattice-based Sampling is used to get the symbols of image so as to make each pixel’s label containing color information rather than only as a class marker. Next, Region map is formed based on Fibonacci Lattice symbols to depict homogeneous regions. On the other hand, by applying fuzzy homogeneity algorithm on the image, we filter it to acquire Edge map. To strengthen the ability of discrimination, both the weighted maps are combined to form Region-Edge map. Based on above processes, growing-merging method is used to segment the image. Finally, experiments show very promising results.

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References

  1. Deng, Y., Manjunath, B.S.: Unsupervised Segmentation of Color-Texture Regions in Image and Video. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 800–810 (2001)

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Author information

Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanhai Jiaotong University, 200030, Shanghai, P.R. China

    Yuchou Chang, Yue Zhou & Yonggang Wang

  2. Department of Computer Science and Engineering, Shanghai Jiaotong University, 200030, Shanghai, P.R. China

    Yi Hong

Authors
  1. Yuchou Chang
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  2. Yue Zhou
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  3. Yonggang Wang
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  4. Yi Hong
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Editor information

Editors and Affiliations

  1. Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia

    Shichao Zhang

  2. Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia

    Ray Jarvis

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Chang, Y., Zhou, Y., Wang, Y., Hong, Y. (2005). A Color Image Segmentation Algorithm by Using Region and Edge Information. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_178

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  • DOI: https://doi.org/10.1007/11589990_178

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30462-3

  • Online ISBN: 978-3-540-31652-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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